Section 2: Poster Abstracts
Abstract no. 7 How to teach health IT evaluation: recommendations for health IT evaluation courses
Elske Ammenwerth, UMIT - Private Universität für Gesundheitswissenschaften, Med. Informatik und Technik Tirol, Hall in Tirol
Nicolette de Keizer, Academic Medical Center, Amsterdam
Jytte Brender, Aalborg University, Aalborg
Catherine Craven, University of Missouri, Columbia
Eric Eisenstein, Duke University Medical Center, Durham
Andrew Georgiou, Macquarie University, Sydney
Saif Khairat, University of North Carolina-Chapel Hill, Chapel Hill
Farah Magrabi, Australian Institute for Health Innovation, Macquarie University, Sydney
Pirkko Nykänen, University of Tampere, School of Information Sciences, Tampere
Paula Otero, Hospital Italiano de Buenos Aires, Buenos Aires
Michael Rigby, Keele University, United Kingdom
Philip Scott, University of Portsmouth, Portsmouth
Charlene Weir, University of Utah, Salt Lake City
High-quality and efficient health care seems not possible nowadays without the support of information technology (IT). To verify that appropriate benefits are forthcoming and unintended side effects of health IT are avoided, systematic evaluation studies are needed to ensure system quality and safety, as part of an evidence-based health informatics approach. To guarantee that health IT evaluation studies are conducted in accordance with appropriate scientific and professional standards, well-trained health informatics specialists are needed. The objective of this contribution is to provide recommendations for the structure, scope and content of health IT evaluation courses. The overall approach consisted of an iterative process, coordinated by the working groups on health IT evaluation of EFMI (European Federation for Health Informatics), IMIA (International Medical Informatics Association) and AMIA (American Medical Informatics Association). In a consensus-based approach with over 80 experts in health IT evaluation, the recommendations for health IT evaluation courses on the master or postgraduate level have been developed. The objectives of an evaluation course are as follows: Students should be able to plan their own (smaller) evaluation study, select and apply selected evaluation methods perform a study and report its results and be able to appraise the quality and the results of published studies. The mandatory core topics can be taught in a course of 6 ECTS (European Credit Transfer and Accumulation System) which is equivalent to 4 U.S. credit hours. The recommendations suggest that practical evaluation training is included. The recommendations then describe 15 mandatory topics and 15 optional topics for a health IT evaluation course. Follow-on activities which are desirable as part of this continuous educational development program are now: consulting a wider stakeholder group on the recommendations, validating the contents though use and review in academic practice, considering the distillation of a subset to form a module on appreciation of health IT evidence, and evaluation in generic health management programs. We invite all teachers of health IT evaluation courses to use these recommendations when designing an evaluation course, to add their course description to, and to report on their experiences. We also invite feedback on the use of the principles of this module as a means of instilling an evidence-based approach to health informatics application in wider health policy and health care delivery contexts. For further information, see https://iig.umit.at/efmi/.
Abstract no. 15 Data-driven pathogen surveillance: linking bacterial genomes with electronic-health data.
Sinead Brophy, FARR Institute (CIPHER - Swansea)Swansea University, Swansea
Guillaume Meric and Samuel Sheppard, Bath University, Bath
Muhammad Rahman, FARR Institute CIPHER (Swansea), Swansea
Introduction Infectious disease remains a major threat to global public health, exacerbated by the rapid development of bacterial resistance to antibiotics. The recent review on antimicrobial resistance calls for the development of surveillance systems to ensure health systems, doctors and researchers can make the most of ‘big data’. This work set out to pilot the development of a pathogen surveillance system through the linking of bacterial genome data with the electronic health record of the individual affected.
Methods 1,000 Campylobacter isolates from 800 people were collected through the Public Health Wales microbiology laboratories over 12 months and labelled with a sample ID number. The identifiers of the infected person (NHS number, name, DOB, address) and sample ID number were held in the Public Health laboratory. The isolates were sent to Bath University for genome sequencing. The identifiable data was sent to a trusted third part within the NHS to assign an anonymised linking field alongside the sample ID. This information was then transferred to the Secure Anonymised Information Linkage dataset to enable linkage to general practitioner and hospital records. This system then allows bacterial genome data linked to an encrypted sample ID to be linked to the patient medical records. This pilot study selected those patients who had cancer (using GP/hospital data) matched for age, gender and socio-economic status with those who had benign tumours. Their encrypted study IDs were unencrypted by the trusted third party to inform the genome sequencing laboratory which samples to genotype. This formed a nested matched case control study.
Results The pilot study identified some issues that would need resolving for the development of a larger surveillance system. For example, infection is common in new-born infants, but these infants often do not have an NHS number, correct baby name and are not registered with a GP. Thus, work is needed to track these samples through the mother if they are to be included in a surveillance system. In this pilot a larger than expected number of patients had a diagnosis of cancer. However, without the source of the sample it is not clear if the majority of samples come from cancer clinics or if this is evidence of infective trigger in the development of cancer. Thus, future work should collect the source (GP, hospital, specialist clinic) of the sample. The results of the case control analysis can be presented in the conference.
Discussion Understanding how bacteria are evolving at the genome-level and how this affects the patient can help understanding of how virulence and antibiotic resistance evolve and how they are being transmitted, help inform public health responses, such as vaccination and infection control programmes and improve the prediction of which drug treatments are most likely to be effective in managing a patient’s infection.
Conclusion This work represents the first step in developing a surveillance system which can examine prescription patterns, characteristics and disease history of the patient and observe and predict their effect on the genetic evolution of human bacterial pathogens.
Abstract no. 22 Supporting utility coefficient elicitation in a shared-decision making context
Elisa Salvi, Enea Parimbelli, Silvana Quaglini, and Lucia Sacchi, Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia
Introduction In shared decision making (SDM), physicians and patients cooperate to refine medical decisions considering both the available clinical evidence and the patient’s personal preferences. Patients’ preferences may be quantified as utility coefficients (UCs), indicators measuring the quality of life perceived by the subject in relation to the health conditions he/she might experience in response to the considered clinical options. To elicit UCs, we developed UceWeb,1 a web-application implementing five elicitation methods: time trade-off, daily time trade-off, standard gamble, willingness to pay, and rating scale. These elicitation methods may suffer from some bias due to specific characteristics of either the patient or the considered health condition. For example, the time trade-off (TTO) method may be inappropriate when the considered condition lasts less than one year.2 The literature suggests that these situations may lead to elicit unreliable UCs, ultimately leading to sub-optimal decisions.
Methods In this work, we propose a rule-based decision support system for targeting the choice of the elicitation method to the considered patient and health state. We formalized twelve rules on the basis of the collected literature evidence. Each rule suggests (or advises against) the use of a specific method when a defined condition is verified. Rule conditions address the patient’s characteristics (e.g. he/she is unemployed) or the health state nature (e.g. it is a short-term disease). For example, one rule advises against the TTO if the considered health state is short-term. We assigned a reliability score to each rule according to the relevance of the supporting evidence. We then implemented an engine that triggers the rules by matching the rule conditions with actual patient and health state data, evaluates the triggered rules, and provides a recommendation for each method.
Results We integrated the proposed system into the UceWeb application. To support elicitation procedures, the recommendation for each method is presented as a traffic light, whose colour summarizes whether the method is suggested or not for the current elicitation. The recommendation also provides the list of the triggered rules, complemented with its supporting evidence.
Discussion For testing purposes, we asked 50 healthy volunteers to elicit UCs for specific health states that are supposed to trigger four of the formalised rules. We are currently analysing the resulting UCs in light of the collected literature evidence. In particular, we are considering whether known unwanted effects on elicited UCs (e.g. saturation of TTO coefficients for short-term diseases) can be observed in our data.
Conclusion To our knowledge, UceWeb is the only elicitation tool providing decision support for targeting the choice of the method to the specific elicitation, facilitating SDM in clinical practice.
References
Parimbelli E, Sacchi L, Rubrichi S, Mazzanti A, Quaglini S. UceWeb: a Web-based Collaborative Tool for Collecting and Sharing Quality of Life Data. Methods Inf Med. 2014 Nov 353(6).
Boye KS, Matza LS, Feeny DH, Johnston JA, Bowman L, Jordan JB. Challenges to time trade-off utility assessment methods: when should you consider alternative approaches? Expert Review of Pharmacoeconomics & Outcomes Research. 2014 May1514(3):437–50.
Abstract no. 24 Electromagnetic interference with medical devices by high-speed power line communication
Eisuke Hanada, Saga Univerity, Graduate School of Science and Engineering, Saga
Kai Ishida, National Institute of Information and Communications Technology, Koganei
Minoru Hirose, Graduate School of Medical Science, Kitasato University, Sagamihara
Takashi Kano, Faculty of Health and Medical care, Saitama Medical University, Hidaka
Introduction High-speed power line communication (PLC) is a communication method that superimposes a signal on the alternating current that flows through a power line. HD-PLC with transmission signals translated in the range from 2 MHz to 30 MHz is mainly used in Japan. Japanese hospitals are resistant to the use of PLC because of government notification of the possibility of interference with medical devices. However, this notification is not based on experimental results or theoretical verification.
Methods The study was done to investigate if PLC would interfere with 20 medical devices in tests of conductive noise and radiated electromagnetic fields, after confirming that the electric power supply was clean. The subjects were five pieces of ultrasonic diagnostic equipment, three electrocardiographs (ECG), two patient monitors, two pulse oximeters, two respirators, and one each of the following: defibrillator, manual defibrillator, electroencephalograph (EEG), medical telemeter, infusion pump, and syringe pump.
Results We found malfunctions of only two pieces of older ultrasonic diagnosis equipment, caused by conductive noise. Superimposed noise was found on one EEG, however, it was only seen when the power line of the PLC modem was intentionally placed in contact with an electrode or terminal box. No noise would be superposed on this type of EEG when in normal use. We did not observe malfunction related to the use of PLC by any of the other tested devices.
Discussion All malfunctions were found on equipment purchased in 2005 or earlier. Equipment manufactured under IEC60601-1-2:2005 or later standards should not be affected. Over 70% of Japanese hospitals use wireless LAN systems for data communication, especially IEEE802.11 series wireless LAN or Bluetooth. The reasons include the difficulty of cable installation and the necessity of accessing the server while moving around the hospital. Additional problems we are concerned about include the possibility of lowered transmission rates and the stoppage of wireless LAN communication by signals invading from outside clinics or hospitals located in heavily populated areas, where the use of wireless LAN has widely spread to homes and businesses. In addition, many APs have been placed throughout cities for public wireless LAN. Wi-Fi routers brought into the hospital by patients are also sources of noise. This is problematic because some hospitals, for financial reasons, use outdated devices.
Conclusion Our results indicate that hospitals should carefully monitor older devices to prevent malfunction. Older devices may be problematic because many hospitals use them because of limited finances. It is also important that hospitals improve the electromagnetic environment of the rooms in which devices with weak biomedical signals are used, whether or not PLC is used.
Abstract no. 28 Motor vehicle crashes and dementia: a population-based study
Lynn Meuleners and Michelle Hobday, Curtin University, Perth
Introduction Demographic changes in the Australian population are leading to an increase in the number of older drivers. Driving is a complex task and requires numerous skills. Some cognitive aspects that are essential for driving such as memory, visual perception, attention, and judgment ability may be affected by dementia. In the early stages of dementia, the risks associated with driving with dementia may go unnoticed due to an average three-year lag between symptoms and diagnosis. This study examined the crash risk among older drivers aged 50+ in the three years prior to an index hospital admission with a diagnosis of dementia, compared to a group of older drivers without dementia.
Method A retrospective whole-population cohort study was undertaken using de-identified data from the Western Australian Data Linkage System (WADLS) from 2001 to 2013. The outcome of interest was involvement in a crash as the driver in the three years prior to a diagnosis of dementia. Logistic regression analysis was undertaken.
Results There were 1,666 (31%) individuals with an index hospital admission for dementia and 3,636 (69%) individuals without dementia who had been involved in at least one motor vehicle crash from 2001 to 2013. The results of the logistic regression analysis found the odds of a crash increased by 77% (odds ratio (OR)=1.77, 95% Confidence Interval (CI)=1.57-1.99) in the three years prior to a hospital admission for older drivers with a diagnosis of dementia, compared to a group without dementia, after adjusting for relevant confounders. Female gender was associated with reduced risk of crash (OR=0.89, 95% CI=0.83-0.95), while having at least one comorbid condition was associated with increased risk of crash (OR=1.09, 95% CI=1.02-1.18).
Discussion Hospitalisation with dementia may represent at pivotal event leading to driving cessation. This may lead to reduced driving exposure especially in risky or new situations. Driving cessation may occur earlier among women, leading to reduced crash risk.
Conclusion Based on the study results and given the increasing number of people who will be diagnosed with dementia, it is important that licensing authorities and clinicians continue to balance safety considerations with mobility needs for older drivers particularly when the early signs of dementia may manifest.
Abstract no. 37 Validation of the recording of asthma diagnosis in UK electronic health records
Francis Nissen, Ian Douglas, and Liam Smeeth, LSHTM, London
Daniel Morales, University of Dundee, Dundee
Hana Muellerova, GlaxoSmithKline, London
Jennifer Quint, Imperial College, London
Introduction The aim of this study is to validate strategies to identify asthma patients in UK electronic primary care records by determining the positive predictive value (PPV) of 8 unique pre-defined algorithms within the Clinical Practice Research Datalink (CPRD).
Methods The PPV is calculated as the number of true positives over the number of positive calls. The positive calls can be found in the database, while the true positives were determined using questionnaires sent to the general practitioners of 880 randomly selected possible asthma patients identified using 8 pre-defined algorithms. The questionnaires were reviewed by two independent experts, one respiratory physician and one general practitioner (GP), to construct a gold standard. The algorithms consist of a combination of one or more of the following: definite or possible asthma Read codes (labels assigned by experts), evidence of reversibility testing and recording of two or more prescriptions of inhaled maintenance asthma therapy, and core asthma symptoms (wheeze, breathlessness, chest tightness and cough).
Results Out of 880 questionnaires distributed, 463 were returned at the time of abstract submission. Of these, 457 were deemed usable and reviewed by two experts. The mean PPV across all of the algorithms was 72 using the study chest physician’s opinion, 71% according to the study team’s GP and 71% in the judgement of the patient’s own GP. The PPVs of the particular algorithms are calculated separately. Based on this preliminary stage of analysis, it appears that a record of definite asthma codes gives a high PPV (81%-85%). Additional conditions of reversibility testing, repeated inhaled asthma therapy, or a combination of all of these three requirements does not improve the PPV. The best PPV (86%-88%) was reached by the combination of possible asthma codes with evidence of reversibility testing and more than one prescription of inhaled maintenance asthma therapy. Algorithms based on asthma symptoms with or without evidence of reversibility testing and inhaled asthma therapy, showed lower PPVs (all less than 60%).
Conclusion This validation study aims to find strategies or algorithms to identify patients with asthma in CPRD. At this preliminary stage, using only definite asthma codes appears the most efficient approach.
Abstract no. 38 Design, construction, acquisition and targeting of resources in the domain of cognitive impairment
Dimitrios Kokkinakis, Kristina Lundholm Fors, Eva Björkner, and Arto Nordlund, University of Gothenburg, Göteborg
Introduction Cognitive and mental deterioration, such as difficulties with memory and language, are typical phenotypes for most neurodegenerative diseases including Alzheimer’s and other dementias. This paper describes the first phase of a project that aims at collecting various types of cognitive data, acquired from human subjects, both with and without cognitive impairments, in order to study relationships among linguistic and extra-linguistic observations. The project’s aim is to identify, extract, process, correlate, evaluate, and disseminate various linguistic phenotypes and measurements and thus contribute with complementary knowledge in early diagnosis, monitor progression, or predict individuals at risk.
Methods Automatic analysis of the acquired data will be used to extract various types of features for training, testing and evaluating automatic machine learning classifiers that could be used to differentiate individuals with mild symptoms of cognitive impairment from healthy, age-matched controls and identify possible indicators for the early detection of mild forms of cognitive impairment. Features will be extracted from audio recordings, the verbatim transcription of the audio signal and from eye-tracking measurements.
Results Currently we do not report concrete results since this is work in progress. Nevertheless, features will be extracted from (i) audio recordings: we use the Cookie-theft picture from the “Boston Diagnostic Aphasia Examination” which is often used to elicit speech from people with cognitive impairments and also reading aloud a short text from the “International Reading Speed Texts” collection, (ii) the manually produced verbatim transcription of the audio: during speech transcription, attention is paid to non-speech acoustic events including speech dysfluencies, filled pauses, false-starts, repetitions as well as other non-verbal vocalizations such as laughing, and (iii) from an eye-tracker: while reading, the eye movements of the participants are recorded while interest areas around each word in the text are defined by taking advantage of the fact that there are spaces between each word. The eye-tracking measurements are used for the calculation of fixations, saccades and backtracks.
Discussion We believe that combining data from three modalities could be useful, but at this point we do not provide any clinical evidence underlying these assumption since the analysis and experimentation studies are planned for year 2 of the project (2017). Therefore, at this stage, we only report a snapshot of the current stage of the work. We also intend to repeat the experiments two years after the current acquisition of data in order to assess possible changes at each level of analysis.
Conclusion We present work in progress towards the design and development of multi-modal data resources and measures (features) to be used both for evaluation of classification algorithms to be used for differentiating between people with mild cognitive problems and healthy adults, and also as benchmark data for future research in the area. Evaluation practice is a crucial step towards the development of resources and useful for enhancing progress in the field, therefore we intend to evaluate both the relevance of features, compare various machine learning algorithms and perform correlation analysis with the results of established neuropsychological, memory and cognitive tests.
Abstract no. 39 Misdiagnosis of COPD in asthma patients in the UK using the clinical practice research datalink
Francis Nissen, Ian Douglas, and Liam Smeeth, London School of Hygiene & Tropical Medicine, London
Hana Muellerova, GlaxoSmithKline, London
Daniel Morales, University of Dundee, Dundee
Jennifer Quint, Imperial College, London
Introduction This study aims to quantify the misdiagnosis of chronic obstructive pulmonary disease (COPD) in asthma patients in the UK using electronic health record databases. The specific objectives of this study are to calculate the PPV, NPV, sensitivity and specificity of a COPD diagnosis recorded by a general practitioner in patients with a confirmed asthma diagnosis. Asthma is difficult to assess in health-care database epidemiological studies as the diagnostic criteria are based on non-specific respiratory symptoms and variable expiratory airflow limitation which are often not recorded in electronic medical records. Specifically asthma in older patients can be confused with COPD.
Methods The 880 asthma patients were identified at random in the Clinical Practice Research Datalink (CPRD) using 8 different algorithms. Questionnaires (110 questionnaires per algorithm) were sent to the general practitioners with a request for asthma diagnosis confirmation to be supported by any evidence available including information on reversibility testing, other factors considered for making an asthma diagnosis, the Quality Outcomes Framework indicators, smoking status, concurrent respiratory diseases and other sources like consultant and hospital discharge letters, lung function tests and radiography results. A review of this information by a respiratory consultant aims to identify the actual cases of COPD in confirmed asthma patients. This review is used as the gold standard to calculate the PPV, NPV, sensitivity and specificity of recorded GP diagnoses of COPD in the primary care records of asthma patients.
Results 463 questionnaires have been returned at the time of abstract submission. Of these, 457 were deemed usable, and 323 asthma diagnoses were confirmed. A co-morbid COPD diagnosis made by a general practitioner in confirmed asthma patients has a sensitivity of 63.6% (28/44), a specificity of 93.9% (262/279), a PPV of 62.2% (28/45) and a NPV of 94.2% (262/278).
Conclusion In this population-based sample of asthma patients, the proportion of patients with COPD that was correctly diagnosed by a GP was 63.6%. The proportion of asthma patients correctly identified as COPD-free by their GP was 93.9%.
Abstract no. 40 Identifying key variables for inclusion in a smartphone app to support clinical care and research in patients with rheumatoid arthritis
Lynn Austin, University of Manchester, Manchester
Caroline Sanders, The University of Manchester, Manchester
Will Dixon, Arthritis Research UK Centre for Epidemiology, The University of Manchester, Manchester Academic Health Science Centre, Manchester
Introduction Treatment for patients with rheumatoid arthritis (RA) is shaped by monitoring changes in disease severity. At present, clinicians have few objective measurements of disease activity between clinic visits, even though a number of patient-reported outcomes measures (PROMs) exist. Smartphones provide a possible solution by allowing regular monitoring of disease severity between clinic visits and integration into electronic medical records. Potential benefits include better information for consultations, triaging of outpatient appointments and aiding patient self-management. Such data could also support novel research by providing temporally-rich data. The REMORA (REmote MOnitoring of Rheumatoid Arthritis) study is designing, implementing and evaluating a system of remote data collection for people with RA for health and research purposes. The project asks whether electronic collection of patient reported outcomes (ePROS) between visits can enhance care and provide a source of research data. This paper describes the process of determining ePROS of importance, and presents the dataset included in the beta-app piloted.
Methods Interviews were held with a range of stakeholders (10 RA practitioners, 12 RA researchers, 21 RA patients). Interviews determined ePROSs for inclusion, recording frequency, and the value of a free text diary. Initially, interviews were conducted with practitioners and researchers regarding their preferences. Key ePROS identified were tabulated and discussed with the PPI (patient and public involvement) group, working alongside the research team, and the table refined. Subsequently, patients were interviewed regarding their preferences and also asked to feedback on tabulated suggestions. Ultimately, components which had widespread consensus across the stakeholder groups were incorporated into the app. Components without consensus, or beyond the scope of the study, were documented with a view to incorporating them in later versions. PPI group members reviewed and commented on the suitability of the final components prior to their incorporation into the beta app.
Results All stakeholder groups wanted to capture information on changes in disease activity and impact of the disease (physically and emotionally). Practitioners and researchers wanted routine data that had been recorded consistently using existing validated tools, but saw the value of a diary for recording triggers and alleviators of disease activity. Patients mainly suggested recording notable events (such as flares) as they occurred, but could see the benefits of recording data routinely. The final dataset comprised the following:
Daily question set: Pain, difficulty with physical activities, fatigue, sleep difficulties, physical and emotional wellbeing, coping (10 point visual analogue scale), morning stiffness (7 categories)
Weekly question set: Number of tender and swollen joints (numeric value 0-28), global assessment of wellbeing (10 point visual analogue scale), employment status (yes/no response - radio button), description of flare (free text box)
Monthly question set: Health Assessment Questionnaire (HAQ) impact of disease on daily activities, including function and mobility (fixed point scales - radio button) plus free text entry box.
Conclusion Consensus on the key components of the smartphone app was achieved. These components have been incorporated into the ‘beta app’ in readiness for piloting within clinical practice.
Abstract no. 46 The multimorbidity model for care coordination by general practitioners
Anna Beukenhorst, University of Manchester, Manchester
Danielle Sent, Academic Medical Center, Universiteit van Amsterdam, Amsterdam
Georgio Mosis, RGA, Hong Kong
Introduction Multimorbid patients, suffering from two or more chronic diseases, often receive multiple disease-specific treatment plans that are likely to contain conflicting recommendations, since medical guidelines typically do not optimally account for complex multimorbid patients. General practitioners (GPs), given their role as care coordinator, are in a good position to identify and reconcile these conflicts.
Method We conducted a literature study and expert interviews to identify practical challenges of guideline-based multimorbidity management in primary and secondary care and existing solutions. Based upon the literature study and interviews, we developed a workflow model providing decision-support for GPs when treating or coordinating care for multimorbid patients.
Results Challenges of multimorbidity care mentioned in literature, were echoed by experts. For example, medical guidelines usually do not account for added complexity (cognitive decline, fall risk, malnutrition and decline in social relations) or conflicting patient preferences.
Competing demands and shifting priorities over time require prioritisation of conditions, revision of treatment plans and ensuring adequate self-management. The conventional workflow of GPs is problem-oriented, hampering a holistic approach. Existing tools for reconciliation of treatment conflicts often focus on specific subpopulations and lack applicability to the generic multimorbid patient population. Models for multimorbidity management, such as the Chronic Care Model and Ariadne principles, only provide abstract advice from an organisational perspective and are not directly applicable in clinical practice.
We therefore propose the MultiMorbidity Model (3M), a framework for CDSSs that supports GPs in delivering multimorbidity care for the comprehensive multimorbid population. It is a workflow model of five steps facilitating identification and reconciliation of various conflicts. It enables a holistic approach and provides opportunities for application of existing computerised decision support tools and shared-decision making tools. GPs take inventory of all applicable treatment recommendations (I – Select), prioritise these based on size of health effect and number of conditions affected (II – Prioritise), and personalise (III) the prioritisation by balancing burden of treatment, personal preferences and expected therapy adherence. The treatment plan is then simplified (IV) by identifying and reconciling conflicting recommendations. Finally, the treatment plan is formulated (V) in a concrete, specific and actionable way, adapted to the patient’s lifestyle and health literacy. Output of the model is an individualized treatment plan for the patient, fitting the patient’s health status, preferences and combination of diseases.
Discussion As a workflow model for multimorbidity management, the 3M provides decision-support to GPs, striking a balance between standardisation of care and personalisation of treatments. A preliminary evaluation indicated that usage of the 3M results in treatment plans with prioritised and concrete recommendations, making it a useful substitute for the usual workflow of GPs during follow-up visits for multimorbid patients. Complemented with shared-decision making tools and computerised decision support tools, 3M enables optimisation multimorbidity care.
Conclusion This is a first step towards CDSSs that facilitate care coordination for multimorbid patients. Future research should focus on further validation of the model, as well as and integration with computerised tools to fit the workflow in the limited time of GP consults.
Abstract no. 48 Capturing provenance of visual analytics in social care needs
Shen Xu, King’s College London, London
Toby Rogers, FACE Recording and Measurement System, Nottingham
Vasa Curcin, King’s College London, London
Introduction The care commissioning (assessment and planning) system currently in operation in England is configured to reduce ‘need’, where need is determined by assessment of the person’s level of impairment, degree of risk/safety, informal care/family support and so on. In order to make cost-effective decisions in social care needs, there are two ultimate questions that need to be answered: what are the classes of individuals with common care needs, and what characteristics determine those classes. Atmolytics is a visualization layer of a data warehouse for social care needs assessment that provides flexible analytic functions to support data analysis. Atmolytics provides the functionality of defining a group of service users by their characteristics as well as their assessment questions and answers. Recorded service user information will be generated lively from shared databases based on the group definition; furthermore the group definition can be further used in the report function. The report function includes 15 types of report that create visual result of group definition. While the analytics required draws on complex real-world data, it is of prime importance to assure that the decisions are transparent and made with correct assumptions. In order to provide transparency and auditability of the tool findings, we have designed a data provenance module within Atmolytics to capture the full audit trail of the data transformations, leading to better understanding of the context of data production.
Method The extended auditing/logging capacity was realized by employing PROV-DM together with provenance template, specifying the structure of data provenance to be captured. The storage solution is designed based on graph database.
Results Initial analysis confirms that capturing provenance in visual analytics should not only describe automated processes but also human actions relevant to the data and models in the system – interactive steps etc. – that are more commonly associated with usability studies. Additionally, current auditing/logging capacity in a typical visual analytics system is insufficient for tracing or representing human actions and supporting a meaningful process mining, more specific it lacks a connectivity of recorded messages. The prototype resulted graph now connects the activities of analytic process within the system. The history of a group definition can be shown as a path from graph database.
Discussion Capturing provenance in a visual analytics system such as Atmolytics is not a trivial task. Each of its subsystems relies on a separate data store, which communicates with others exclusively via a service bus architecture. Furthermore, disconnected auditing/logging functions expose different levels of events. In order to overcome these issues, we are employing provenance templates as higher-level abstractions over provenance graph data, implemented through a dedicated module that communicates with all other parts of the system. Future research plan is provenance data visualization by clustering and RDF database comparison on provenance use cases.
Conclusion We find provenance template approach to be a realistic and promising solution to improving auditing/logging capability in enterprise visual analytics software, and we are currently in the process of developing the provenance visualization tool.
Abstract no. 52 Developing a genetic analysis system for clinical purposes
Espen Skorve, Aalborg University, Department of Computer Science, Aalborg
Morten C. Eike, Tony Håndstad and Thomas Grünfeld, Oslo University Hospital, Oslo
The significance of genetic testing for clinical purposes is increasing, and with the introduction of high-throughput sequencing (HTS) techniques and tools, this development is escalating. However, utilizing the output of sequencing – regardless of techniques and tools – requires a complex, multi-stage analysis process. The genetic variants in a patient must be identified and compared to variants that have been previously encountered in other individuals and current knowledge about their clinical significance. For variants that have not been previously described or for which there is limited clinical evidence, several predictive algorithms may be deployed, depending on the particular sequence context. Hence, the advancement in sequencing techniques must be complemented by technologies that can support end users in exploiting and producing information. This paper presents a user-driven innovation project at Oslo University Hospital, Norway, aimed at developing such technologies.
User-driven innovation has been described as a pull away from the traditional technology-centered innovation strategies, towards strategies that aim at achieving a co-evolution of the technical and the social, where users play a crucial role. This development has also been characterised as a democratisation of innovation, and it is interesting to note how both European Actions and priorities (e.g. in H2020) and various national priorities around Europe (and also in other parts of the world) actively seek to stimulate user-driven innovation through financial resources. The project we report from is a result of such an initiative in Norway the Norwegian clinical genetic Analysis Platform (genAP) project was funded by the Norwegian Research Council as a user-driven innovation project in collaboration between the Department of Medical Genetics (DMG) at the Oslo University Hospital and the Department of Informatics at the University of Oslo. It ran from 2011 to 2015, as a multidisciplinary collaboration between experts of multiple domains (medical genetics, molecular biology, bio-informatics, information systems, etc.) including users and the University’s IT-department as supplier of a secure environment in which the system could run. The target system’s character as a decision support tool, embedding highly specialized knowledge from all these domains, made this composition of project participants and associates pivotal for achieving the overall aims of the project.
Being publicly funded through the National Research Council and carried out as a collaboration between researchers and users, the genAP project constitutes a particular kind of user-driven innovation. As such, it also illustrates how this configuration can meet some of the challenges associated with user-driven innovation. First, both the public funding and the contribution from researchers of competencies and skills that would otherwise be bought on the open market entails a significant cost reduction for the user organisation. This makes it more likely that innovation will actually be realised. Secondly, the diffusion of innovation is enhanced through the reporting of research results and future research based on these results. When the user network is supplemented by a research network, the number of channels for diffusion is significantly increased, increasing the potential for creating public value beyond the specific innovation context.
Abstract no. 55 Towards a clearer vocabulary for clinical knowledge representations: being more precise than “ontology”
Alan Rector, University of Manchester, Manchester
Jean Marie Rodrigues, INSERM U 1142 Paris France, Saint Priest en Jarez
Christopher Chute, Johns Hopkins University, Baltimore, MD
Introduction We suggest an alternative vocabulary for describing different types of knowledge and knowledge representation that maps conveniently onto current technologies and avoids arguments about “what is ontological.” This paper contends that the term “ontology” is being used in so many different ways that it has lost most meaning except for indicating a knowledge representation involving a hierarchy. We take as our starting point an analogy with existing paper resources. We can divide paper knowledge resources into at least four groups: dictionaries, encyclopedias, catalogue/indexes/thesauri, manuals and records.
Results We suggest avoiding the words “ontology” and “ontological” except as broad headings and instead distinguishing: 1. Axiom-Base/open world component – rather than “ontology” or “ontological”, for first-order axiomatic knowledge. 2. Generalization base/closed world component – for other kinds of knowledge that admits exceptions. 3. Knowledge organization base – for other more loosely specified information and human navigation including thesauri. 4. (Statistical) Classification – for mono-hierarchical organizations for specific purposes, usually statistical reporting, following the jointly-exhaustive-mutuallyexclusive rule. 5. Representation knowledge base – for whatever queries or other knowledge about the representation is required for the system to function. 6. Higher order knowledge base – for whatever higher order generalizations and axiomatic knowledge is relevant & feasible to represent. 7. Rule base or decision support systems. 8. Record repository – for records of individual patients for care or research.
Abstract no. 59 Comparative effectiveness of non-vitamin K antagonist oral anticoagulants and warfarin in the Scottish atrial fibrillation population: the value of real world evidence
Giorgio Ciminata, Claudia Claudia Geue, Olivia Wu, and Peter Langhorne, University of Glasgow, Glasgow
Introduction Clinical data from randomised control trials (RCTs), providing evidence on efficacy, have been used to inform economic evaluations of non-vitamin K antagonist oral anticoagulants (NOACs) in atrial fibrillation (AF). In contrast, real world data offer the advantage of providing evidence on the effectiveness of NOACs in clinical practice. However, the absence of randomisation in a real world scenario does not allow for an unbiased comparison between the treatment and the comparator. The difference in observed health outcomes between the two groups may be due to patient case mix rather than treatment effect. The aim of this study is to explore, within the comparative effectiveness framework, different methods for estimating average treatment effect (ATE), and assess whether the findings reported in RCTs are generalizable to Scottish clinical practice.
Methods Based on the review of propensity score methods and the nature of administrative data available, the matching by propensity score approach will be explored. With this method, the difference in ATE is given by the difference in outcomes between groups matched according to their propensity score. The matching will create a subsample of individuals on NOACs or Warfarin who share a similar propensity score value. The ATE for the subsample will be estimated for a cohort of patients 50 years and older, hospitalised with a known diagnosis of AF or atrial flutter. Event rates, for ischaemic stroke (IS), clinical relevant bleeding (CRB), intracranial haemorrhage (ICH) and myocardial infarction (MI), for the NOACs and Warfarin cohorts, will be compared the relative effectiveness taking into account potential confounders such as geographical differences in comorbidities and prescribing preferences will be assessed.
Results The comparative effectiveness analysis is currently underway and results will be presented at the conference.
Conclusions This work will form part of a wider economic evaluation of NOACs for the management of AF. The estimated ATE and clinical event rates will be compared against results from RCTs, and in conjunction with AF related costs will inform a cost-effectiveness model.
Abstract no. 60 Literature review of potential use errors of adrenaline auto-injection pens
Thomas Weinhold, Marzia Del Zotto, Jessica Rochat, and Christian Lovis, Geneva University Hospitals (HUG), Division of Medical Information Sciences (SIMED), Geneva
Jessica Schiro, Sylvia PELAYO, and Romaric Marcilly, University of Lille, INSERM CIC-IT 1403, CHU Lille, EA 2694, Lille
Introduction The project Useval-DM aims to establish scientific evidence of critical methodological choices for usability validations. Different variables and their influence on evaluation results are analyzed (e.g. number of participants, fidelity of the testing environment, cultural differences). In this context, three different medical devices are evaluated. One is an innovative needle-free self-injection device. To get an overview of usability-induced use errors of such devices a literature review was conducted.
Method Since there is no literature about needleless systems, due to the novelty of this technology, needle-based auto-injectors had to be considered for the review. The analysis was based on PubMed and Scopus and encompassed original studies reporting on the usability of auto-injection pens that were published in English or French from 2000 to 2016. For the research we used a building blocks approach. Three sets of different key terms (type of technology usability safety/errors) and synonyms were defined and combined with Boolean operators.
Results 1282 papers were identified, from which 310 were duplicates. The remaining 972 papers were screened for their relevance based on three iterations with an increasing degree of accuracy. First, one expert screened the titles and excluded those papers not matching the eligibility criteria. Then two reviewers checked the abstracts of the remaining documents. Finally, the remaining papers were analyzed by three experts. As a result, 24 documents, as well as 9 additional papers identified by searching references, were considered for a qualitative analysis. Each paper was examined by one expert and results from the extraction were cross-checked by the other experts. Extracted descriptions of use errors were categorized to obtain a list of known use errors for such devices. Overall, 22 categories of use errors could be identified. Examples are errors related to the storage and checking the integrity of the device before an injection (Schiff M et al. (2016) Adv Ther), problems with safety caps and in orienting the pen during an injection (Schmid M et al. (2013) Open Allergy J), as well as issues with the injection itself (e.g. injection site, duration) (Guerlain et al. (2010), Ann Allergy Asthma Immunol).
Discussion The aim of this study was to identify and classify potential use errors related to the use of a needleless auto-injection device. Since there is no literature about such devices yet, we had to widen our research to common auto-injection pens. Therefore, the results could not be transferred directly to the needleless system. Rather a triangulation with other methods and sources (e.g. incident reports databases, interviews, observations) must be made.
Conclusion Literature reviews are an indispensable source for the design and usability validation of medical devices. But for innovative products they can only deliver a brief overview of potential problems. For such devices it is essential to take into account their specific characteristics. However, even if the devices in the literature differed from our product, the reports were helpful, since it was easier to make an abstraction than to consider all possible risks independently.
Abstract no. 65 Prescriptions dispensed in the community pre- and post-cancer diagnosis in England
Katherine Henson, Victoria Coupland, and Rachael Brock, National Cancer Registration and Analysis Service, Public Health England, London
Brian Shand and Kelvin Hunter, National Cancer Registration and Analysis Service, Public Health England, Cambridge
Philip Godfrey, NHS Business Services Authority, Newcastle
Background The National Cancer Registration and Analysis Service (NCRAS) in Public Health England has, for the first time, received pseudonymised national record-level data on prescriptions dispensed in the community, as part of a partnership project with the NHS Business Services Authority (NHSBSA). This partnership will allow us to answer clinical questions that have not been possible previously. For a pilot study of this partnership, the authors aim to describe the symptom profiles of cancer patients in the time surrounding a cancer diagnosis, and the variation by cancer stage.
Method NCRAS registers all cancers that occur in people diagnosed throughout England. Prescription data is extracted from the NHS prescription payment processes managed by NHSBSA. A pseudonymisation procedure, which uses standard third-party encryption and hashing modules, was applied to the national cancer registration data and the prescription data to allow secure linkage. All malignancies, excluding non-melanoma skin cancer (NMSC) (ICD10 C00-C97 excluding C44) with a diagnosis date between January and October 2015 were included in the analysis. The prescription data sample covers the period April to July 2015. Patients were only counted for the months in which they had a prescription. The prescription date was compared to the cancer diagnosis date recorded by NCRAS to create a peri-diagnosis timeline.
Results Investigation of the most common medication groups highlighted a marked variation by cancer stage for all malignancies combined. For patients with stage 1 and 2 cancers, the most commonly prescribed medication group was lipid-regulating drugs, followed by proton pump inhibitors (PPIs). There were no marked trends over the six-month period.
Among patients with stage 3 and 4 cancers, there was a substantial upward trend over the time period surrounding the diagnosis date for opioid analgesics and enteral nutrition.
Discussion The most commonly prescribed medication groups for patients with stage 1 and 2 cancers are consistent with those frequently prescribed to older patients. The medication groups with the most marked trends for patients with stage 3 and 4 cancers appear to be associated with the effects of cancer and its treatment. The prescribing patterns are consistent with increased pain. Similarly, enteral feeding prescriptions may reflect weight loss, nausea or difficulty swallowing. Further work will extend this descriptive analysis with statistical adjustment of case mix and investigation by cancer type.
Conclusion Prescriptions data is a hugely rich source of information, and provides us with many potential new areas of research. The investigation of quality of life endpoints using symptom profiles presented here are important to patients.
Abstract no. 71 Integrating an mHealth application into the EHR ecosystem of Andalusian health public system
Alicia Martínez-García, Technological Innovation Group. Virgen del Rocío University Hospital, Seville
Rafael Ordoñez-Benavente, Group of Research and Innovation in Biomedical informatics, Institute of Biomedicine of Seville, IBiS/Virgen del Rocío University Hospital /CSIC/University of Seville, Seville
Santiago Rodríguez-Suarez, Juan Antonio Grande-Navarro, and Carlos Parra, everis Spain S.L., Seville
Sergio Barrera-Benitez, Integral Healthcare Unit, Virgen del Rocío University Hospital, Seville
Introduction The HEARTEN project aims to develop and validate a collaborative mHealth application that engages all actors related to the management of Heart Failure disease, enables the patients to achieve sustainable behaviour change regarding their adherence, and improve patients’ quality of life. As part of this project, a specific task covers the integration of this mHealth application with the Electronic Health Record Ecosystem (named Diraya) of Andalusian Health Public System (AHPS).
Method To perform this integration, three high-level integration needs have been identified: (i) Accessing HEARTEN platform from Diraya, (ii) Drug prescription in Diraya will be communicated to HEARTEN platform, (iii) Reports of information stored in HEARTEN will be retrievable in Diraya as an external report making use of the API provided by HEARTEN.
Results So far the architecture design and the functional requirements of the integration solution have been defined. This architecture is prepared to integrate HEARTEN within Diraya by making use of different mechanisms: (i) HL7 to communicate to Diraya new reports. HL7 is also used with Andalusian ePrescription (primary healthcare electronic prescription) to get information from prescribed drugs of patients that are being monitored in HEARTEN. (ii) REST API to communicate with HEARTEN platform in order to retrieve data for new reports and to include information concerning the drug prescription collected from Diraya. (iii) Web Services to extract data from the AHPS hospital healthcare electronic prescription concerning patients monitored in HEARTEN. Also, to allow the access from Diraya to HEARTEN doctor’s user interface (UI), a HTTPS gateway will be deployed to allow the connection for these systems. Finally, in order to monitor the integrations and ease and support the management of AHPS staff involved in the project, a specific multi-language dashboard application is being developed to provide reports and alerts concerning the state of the integrations. In order to orchestrate those heterogeneous communications, an integration layer will be deployed. The architecture relies on an Oracle database to store the exchanged information and customization parameters. A JEE application deployed under Apache Tomcat will support the dashboard application, where PrimeFaces provides the UI, Spring is used for business and Hibernate gives the tools for persistence. At this moment, the technical team is working to implement the solution.
Discussion The participation of clinical teams in research and innovation projects causes the use of several different informatics systems by the healthcare professionals, including the obligation of recording the same information in different systems, and a tedious management of usernames and passwords. The integration of the specific application developed in the project with the local EHR corporative services solves this problem.
Conclusion The implementation of an integration framework between HEARTEN mHealth application and Diraya achieve a more useful final solution for the doctors, avoiding spending unnecessary time by using several informatics systems, caused by duplicate information in different systems. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 643694, and FEDER funds.
Abstract no. 90 The eLab platform: realising reproducible collaborative analyses of harmonised data
Chris Munro, Philip Couch, Andrew Broadbent, Paul Stephenson, Stephen Lloyd, Ruth Norris, Clare Mills, John Ainsworth, and Iain Buchan, University of Manchester, Manchester
Danielle Belgrave and Adnan Custovic, Imperial College London, London
Introduction Scientific journal publications seldom capture scholarly work so that an external researcher could reproduce reported results. The growing number of platforms for sharing scholarly assets (e.g. Figshare, ReShare, and EPrints for research data) reflects this, presenting science a sizeable opportunity to reuse resources of previously funded projects to make new discoveries, especially across disciplines/ domains.
Despite the plethora of platforms and standards, barriers remain to the reuse of scholarly assets due to missing information about datasets and their analyses. Poorly curated data leads to unnecessarily duplicated efforts in reusing scholarly assets and data harmonization and analysis are often manually repeated when reusing data. We discuss a methodology for combining and harmonizing research data, supporting the capture and sharing of rich contextual information essential for correct interpretation and data re-use.
Methods Minimum Information Checklists have been applied to develop Variable Reports and Variable Report Models: the mechanism through which we incorporate data context and achieve semantic harmonization across different sources of comparable data, e.g. different birth-cohort studies. eLab communities use Variable Report Models to specify reporting requirements for variable summaries which are used when reporting data.
Research Objects have been applied alongside new distributed computing technologies in the Job Manager Module to support the reproducibility of analyses, which might be reported in publications. This includes moving code and input files to a remote resource through HTCondor, providing process statuses and moving outputs back into the eLab.
Results The HeRC eLab builds on the Alfresco Enterprise Content Management System, which provides core support for day-to-day project activities such as document management, audit history, and collaboration. We developed the Variable Bank Module which facilitates the import and export of data. Research questions can be asked across multiple datasets created by different communities. The Job Manager module works with data and computational code through Execution Research Objects which capture the process of executing analysis code on data. The eLab has been further developed and successfully used by the STELAR (Study Team for Early Life Asthma Research) consortium to harmonise data from five UK birth cohorts and the iFAAM (integrated approaches to food allergy and allergen management) EU project to harmonise data from food allergy studies.
Conclusion We have presented Variable Reports and Execution Research Objects as approaches to capturing context and provenance of data and methods and their application within the HeRC eLab. Our work distinguishes itself from existing approaches, providing:
a way to combine and pool datasets from any domain
data search, combine and export capability from multiple studies automatically, with fewer manual processes to query and deliver data extracts.
Often, data warehouses and other approaches, also extract, transform and load data, without capturing the relationships between variables–the essence of any research claim. Variable Report Models advance semantic harmonization-with no further cost for reuse after the initial investment. The use of Minimum Information Checklists with Variable Reports means that software does much of the metadata quality checking.
Abstract no. 91 Intelligent assistance services and personalized learning environments for support of knowledge and performance in interdisciplinary emergency care
Sabine Blaschke, Bjoern Sellemann, Stefanie Wache, and Stefan Roede, University Medical Center Goettingen, Interdisciplinary Emergency Care Unit, Göttingen
Michael Schmucker, Heilbronn University, GECKO Institute, Heilbronn
Carsten Ullrich, Michael Dietrich and Christoph IGEL, German Research Center for Artificial Intelligence, Educational Technology Lab, Berlin
Markus Roessler, University Medical Center Goettingen, Dept. of Anaesthesiology, Göttingen
Sabine Rey, University Medical Center Goettingen, Inst. of Medical Informatics, Göttingen
Martin Haag, Heilbronn University, GECKO-Institute, Heilbronn
Felix Walcher, University of Magdeburg, Dept. of Trauma Surgery, Magdeburg
Introduction During the past decade emergency medicine evolved to an increasing challenge for clinics of all stages of patient care due to a substantial and continuous change of medical knowledge, limits of time and health care economics as well as an enormous rise of patient cases. Thus, continuous medical education for all employees involved in the preclinical or clinical phase of emergency care represents an essential prerequisite for high quality patient-centred care to overcome these problems. However, in this special setting of rush, stress and highly intense workload, conventional learning techniques do not allow for continuous training on the job. To address this problem we developed novel learning and teaching strategies based on digital technologies for both academic and non-academic staff members within interdisciplinary emergency care departments (ED).
Methods For medical students and trainees we created a podcast and an emergency care software for simulation of emergency cases in order to prepare for the work within the ED in comparison to control groups without access to these learning tools. Acceptance, frequency of usage and effects of these techniques were assessed prior to and after the occupation within the ED by standardized questionnaires and tests. For nurses and paramedics we first assessed the information demands during all processes of emergency patient care in the preclinical and clinical phase. Based on these needs intelligent assistance services were established in cooperation with two technological partners to support daily workflow via web-based services.
Results Introduction of the podcast and the emergency care software prior to the start within the ED resulted in a significant improvement of skills and expert knowledge for both medical students and trainees in comparison to the control groups (p< 0.002). Both innovative tools were widely accepted and frequently used. Analysis of processes within the preclinical and clinical phase of emergency care revealed information demands for paramedics and nurses especially with respect to invasive/non-invasive techniques, first aid standard operating procedures for leading symptoms, medications and medical devices. Assistant information, process, simulation, documentation as well as collaboration services were then developed for web-based usage via mobile devices (tablets) within defined use cases including cardiopulmonary resuscitation. Assistant services and personalized learning environments will now be evaluated by analysis of utility, usability, acceptance and learning efficiency in a pilot study starting in March 2017 within two different EDs.
Conclusions Introduction of novel learning and teaching strategies within the ED allows for a continuous medical education and training on the job in the special setting characteristics of emergency care. Results of our studies revealed a significant improvement of technical skills and medical expertise thus leading to a better performance of the academic staff within the ED. Further studies with non-academic employees now have to evaluate the effects of these innovative strategies within the preclinical and clinical phase of emergency care.
Abstract no. 102 A literature review to define concepts and dimensions of ecological validity/fidelity for usability validation
Jessica Rochat, Thomas Weinhold, and Christian Lovis, University Hospitals of Geneva and University of Geneva, Geneva
Jessica Schiro, Romaric Marcilly, and Sylvia Pelayo, CIC IT Lille / EVALAB, Lille
Introduction While quantitative evaluations, such as clinical trials, are well known and formalized, the situation is different for qualitative evaluations such as usability validation: they are insufficiently defined despite the imposition of the EU Medical Device (MD) Directive. The challenge for usability validation of MD is to make sure that risks of use errors identified during simulation-based usability testing are effectively representative of risks of use errors when the device is used in real settings.
In order to establish scientific evidence of critical methodological choices for usability validations environments, we addressed the question of the cost-effectiveness ratio of varying the level of realism of simulation-based usability validation. This study presents the first part of the research, which consists in a literature review to identify relevant dimensions of the environment that can be varied to assess their ability to identify use errors along with their costs.
Method Two databases were searched: Pubmed and Scopus. Three sets of key terms were defined to specify keywords for (i) evaluation, (ii) usability and (iii) ecological validity/fidelity. One reviewer performed the query and screened titles and abstracts. The identified papers were cross-checked by a second reviewer (K=0.9). The read-through of selected full-text papers was performed independently by two reviewers. In case of disagreements, the inclusion eligibility was discussed with a third reviewer.
Results Only 12 papers specifically discussed dimensions of ecological validity, which may influence the results of simulation-based usability testing. Ecological validity refers to what extent the test environment mirrors the environment in which a product would be used in ”real life”, i.e. to the extent to which experimental findings can be generalized to everyday life. It implies the possibility for participants to be able to apply their expertise during the experiment and to use the product as they would have done in ‘real life’. The fidelity of a simulation-based usability testing environment is a measure of its ecological validity. The dimensions of ecological validity are roughly different depending on the authors. But all the authors agree on three critical dimensions: task, prototype and environment fidelity. Three other dimensions are also highlighted: scenario, behaviour and users fidelity. Only two dimensions can be retained to test their impact on simulation-based usability validation: the environment and behaviour fidelities. The dimensions related to users, task (scenario) and prototype fidelity could not be tested due to the regulation constraints of the experimental design: final versions of the MDs must be evaluated, real users need to be included, and the task fidelity must be defined according to potential use errors to test.
Discussion & conclusions The literature review allowed to identify two dimensions of ecological validity which should be considered during our simulations. By varying them, their influence on the cost-effectiveness ratio of usability validations will be analyzed. For the environment fidelity, this includes simulating the environment’s stimuli (e.g. noise, odours), equipment (e.g. artefacts, medical equipment) and the room (e.g. home, lab, meeting room). With regard to the behaviour, verbalizations of actions (low-fidelity) will be compared to actually performed actions (high-fidelity).
Abstract no. 107 Methods for enhancing biomedical research data discoverability
Christiana McMahon and Spiros Denaxas, University College London, London
Arofan Gregory, Open Data Foundation, Tucson
Tito Castillo, University College Hospital, London
Introduction Diverse and disparate datasets are increasingly being linked and used in research both at scale and at higher clinical resolution. In biomedical research, a growing ‘open’ research culture has emphasised the significance of publicly-accessible metadata the availability of which is critical since researchers use clinical datasets containing personally identifiable information, and are not always able to readily share these data.
However, the inability to characterise and evaluate datasets due to insufficient metadata limits the extent to which data may be utilised for research. These challenges are compounded by inconsistencies in the way researchers record and share discovered datasets. This study aimed to identify and evaluate methods to enhance biomedical research data discoverability.
Methods We used a combination of analytical techniques: a systematic literature review to characterise existing data discoverability practices and identify current challenges an online international stakeholder survey and feasibility analyses (technical, economic and organisational factors) of methods to enhance biomedical research data discoverability.
Results We identified 49 studies and organisations 13 were randomly selected for review. PDF was the most commonly used format for research protocols whereas research data were mostly disseminated using SAS, STATA and SPSS files. A total of 253 individuals completed the survey. The most popular aspect of a research study that should be easily searchable was the ‘research study question’ (15%). Survey results showed that variable standards of data management and research data negatively impacted the handling of metadata. Challenges associated with data publications included, limited perceived significance and the need for changes in research culture for data to be, “considered and acknowledged as a valuable scholarly output alongside publications”. However, formal academic recognition of their significance is limited and the publishing of these articles could have an associated open access fee. Semantic web technologies, e.g. the Resource Description Framework, use uniform resource identifiers to differentiate between disparate data sources which may be integrated. However, limited familiarity with these technologies could result in a significant demand for training. Public health portals: online catalogues of metadata records describing studies for which research data may be available for reuse. Researchers are already using online portals yet, integrating use of this portal into work routines may be challenging additional resources are required to develop and sustain the portal.
Discussion We identified inconsistencies in how research data were documented (e.g. the provision of online data dictionaries) and the creation/usage of metadata. Most of the survey respondents were data users and given that awareness of the significance of having high-quality metadata is still increasing amongst researchers, these results could be attributed to limited awareness of the discoverability issue and inadequate routine metadata administration.
Conclusion Our findings suggest that more emphasis is needed on the importance of metadata through training/support the advantages of data publications and increased recognition of these outputs within the academic community. The three methods identified and evaluated can support these recommendations.
Abstract no. 114 Determining the accuracy of routinely-collected health datasets for identifying neurodegenerative disease cases: UK biobank approach
Tim Wilkinson, Amanda Ly, Zoe Harding, Christian Schnier, and Cathie Sudlow, University of Edinburgh, Edinburgh
Introduction Neurodegenerative diseases such as dementia and Parkinson’s disease (PD) are major causes of mortality and morbidity. Prospective cohort studies can provide important insights into the determinants of these disorders. UK Biobank (UKB) is a large, population-based, prospective cohort study of over 500,000 participants aged 40-69 years when recruited between 2006 and 2010. Participant follow-up is largely via linkage to routinely-collected health datasets such as hospital admissions, death registrations and, increasingly, primary care data. Here, we discuss the approach we have developed to estimate the accuracy of these sources for the identification of dementia and PD outcomes.
Methods We conducted systematic reviews of studies that assessed the accuracy of ascertaining dementia or PD cases from codes in routinely-collected datasets versus a clinical expert diagnostic reference standard. We summarised results for positive predictive value (PPV) and sensitivity. Informed by these results, we performed our own validation study of dementia coding using data from UKB participants and have commenced a similar study for PD. Using published and online resources and clinical judgement we generated a list of ICD-10 and primary care (Read version 2) dementia codes. We identified Edinburgh-based UKB participants with a dementia code in hospital admissions, death or primary care data. We extracted relevant letters and investigation results from the electronic medical record (EMR). A neurologist adjudicated on whether dementia was present based on the extracted notes, providing the reference standard to which the coded data were compared. We calculated the PPV for each data source individually and combined.
Results The systematic reviews revealed a wide variation in methodologies and results across existing studies in the literature. For PD, PPVs ranged from 71-88% in hospital and death datasets, while in a primary care dataset the PPV was 81%, increasing to 90% in patients who also received >1 prescriptions for antiparkinsonian drugs. Sensitivities for PD coding in hospital and death datasets ranged from 53-83%. PPV estimates for dementia coding in hospital and death datasets ranged from 4-100%, with PPVs of 83-92% for primary care data. The use of specific subtype codes or selection of codes in the primary position only resulted in higher PPVs; however, there was a corresponding reduction in case ascertainment. For our UKB validation study of dementia coding, there were 17,000 Edinburgh-based participants of which 44 participants had a dementia code in at least one data source and available EMR data. PPVs for dementia were 41/44 (93%, 95% CI 81-99) overall, 13/15 (87%, 95% CI 60-98) for hospital admissions, 2/2 (100%, 95% CI 16-100) for deaths and 33/34 (97%, 95% CI 85-100) for primary care data.
Discussion Results to date suggest that, with appropriate choices of codes, the diagnostic accuracy of these datasets is likely to be sufficient for identifying dementia and PD cases in large-scale, prospective epidemiological studies. Primary care datasets are potentially valuable data sources warranting further investigation.
Conclusion By systematically reviewing the literature and performing our own validation study, we have developed a method of estimating the accuracy of using routine datasets to identify neurodegenerative cases.
Abstract no. 122 Presentation of laboratory test results in patient portals: effect on risk interpretation and patient interaction.
Paolo Fraccaro, Lamiece Hassan, Grahame Wood, and Iain Buchan, Health eResearch Centre, Farr Institute for Health Informatics Research, The University of Manchester, Manchester
Panos Balatsoukas, NIHR Greater Manchester Primary Care Patient Safety Translational Research Centre, The University of Manchester, Manchester
Sabine van der Veer, Richard Williams, Smeeta Sinha, and Niels Peek, Centre for Health Informatics, NIHR Manchester Biomedical Research Centre, The University of Manchester, Manchester
Introduction Patient portals are considered valuable conduits for supporting patients’ self-management. However, there are safety concerns over how patients might interpret and act on the information from these portals, especially laboratory test results. Contemporary information visualisation research has produced methods that improve human perception and cognition in different information seeking and decision-making tasks. However, these methods have not been evaluated for presenting clinical test results via patient portals. Our objective was to investigate the effect of different visual presentations of laboratory results on risk interpretation and user interaction.
Methods We conducted a controlled study with 20 patients with a kidney transplant, who had quarterly blood tests. Participants visited our human computer interaction laboratory and interacted with different clinical scenarios, designed by nephrologists, to reflect high, medium and low health risks. These were composed of results for 28 different laboratory tests and shown using three different web-based presentations. Each presentation was tile-based, with a baseline presentation (based on Patient View, a system currently available to patients) and two more advanced presentations providing different visual cues, colours and tools to show normal and abnormal values. After viewing each clinical scenario, patients were asked how they would have acted in real life: 1) call their doctor immediately (high perceived risk) 2) ask for an appointment within four weeks (medium perceived risk) or 3) wait for their next scheduled appointment (low perceived risk). We tested each presentation in terms of accuracy of risk interpretation, perceived usefulness, level of understanding, information processing, and visual search behaviour.
Results We found no statistically significant differences between the three presentations in terms of the accuracy of risk interpretation. Misinterpretation of risk information was high, with 65% of patients underestimating the severity of risk across all presentations at least once. Particularly, patients decided to wait for their next appointment in 50% of the medium and high-risk cases. Patients found it particularly difficult to identify medium risk. The two advanced presentations were perceived as more useful (P=0.023). Differences in information usage and level of information processing were associated with personal characteristics, such as previous experience with PatientView, frequency of internet usage, education and graph literacy. Overall, patients followed similar visual search behaviours across the three presentations. The comparison of longitudinal information for two laboratory tests was rarely used. Patients who interpreted information correctly adopted more targeted visual behaviours than those who did not, focusing on relevant test results for their condition.
Discussion Although limited by a small sample size, our study is the first to investigate the effect of information visualisation design on patients’ interpretation of risk when accessing realistic panels of laboratory results online. Our study provides also unique data on how patients interact with and make sense of laboratory results in patient portals.
Conclusions This study confirms patients’ difficulties in interpreting laboratory results, with many patients underestimating risk across different presentations, even when abnormal values were highlighted or grouped.
Abstract no. 132 Potential use errors of ANI monitor to evaluate patient pain and discomfort
Marzia Del Zotto, Thomas Weinhold, Jessica Rochat, Christian Lovis, Division of Medical Information Sciences (SIMED), Geneva University Hospitals (HUG), Geneva
Pierre-François Gautier, Jessica Schiro, Sylvia PELAYO and Romaric Marcilly, Univ. Lille, INSERM CIC-IT 1403, CHU Lille, EA 2694, Lille
Introduction The correct identification and classification of use errors is crucial in evaluating the usability and the safety of a medical device. This identification proceeds through a detailed analysis of scientific literature of similar devices, context of use, feedbacks from manufacturers, and incident report databases. The main goal of this study was to identify and classify potential use errors related to the use of innovative pain monitor (ANI Monitor). The calculations have been designed in order to objectively rate the level of the patients’ pain and their own comfort/discomfort by means of an electrophysiological signal (Analgesia Nociception Index - ANI).
Method Several sources of information have been inspected in agreement with European and International guidelines. We collected data coming from: i) similar devices via journals and conference proceedings as well as safety reports [e.g. Marcilly R. et al. (2014) in Stud Health Technology Inform Lieblich SE (2004) in Journal of Oral and Maxillofacial Surgery] ii) previous Human Factors (HF) analyses iii) interviews with end users having high expertise in monitoring pain with conscious and unconscious patients iv) observations at the intense care unit of two different hospitals, respectively in France and in Switzerland. Results were gathered and synthesized through a Failure and Effect Mode Analysis (FMEA) to identify the potential use errors.
Results A total of 11 potential use errors were identified and classified by means of a level of severity based on their own safety-related consequences. They deal mainly with misinterpretation of the Index, with misunderstanding between two Indexes (ANI mean and ANI instantaneous), and with unawareness of the poor quality of the ECG signal. Consequently, their effects on patients include an unsuitable treatment leading to an over- or under-dosage of analgesic drugs.
Discussion In our study, the identification of use errors arises from the integration of different sources (e.g. context of use, observations of the real environment, interviews with experts as well as incident report databases from manufactories), since no literature is available yet, due to the novel nature of this technology. Consequently, by means of FMEA, we systematically classified and prioritized potential failures and their effects on patients to design possible scenarios of user tests.
Conclusion The integration of different sources, as well as literature review, context of use, manufacturers’ feedback and incident report databases, is essential in the user-centred design for the usability validation of medical devices. It allows identifying risks and use errors of the device itself. However, in our case, we could identify some explicit failures taking in account the specific context of use and the functional characteristics of the pain monitor, given that no literature of similar devices was available. The most common use errors will be tested during the summative evaluation of diverse scenarios having different levels of fidelity.
Abstract no. 134 Developing analytical capability in health care in the UK
Martin Bardsley, The Health Foundation, London
Introduction The impact of advances in data science is often dependent on the capacity and capability of analysts working in the health care systems who have to implement new approaches. In this presentation, I will discuss work undertaken at The Health Foundation to outline the challenges facing the analytical workforce in the health services in the UK.
Methods Qualitative analysis of 70 interviews with analysts, academics, clinicians and managers working for health services and public health in the UK during 2016.
Results Though there are examples of good analysis and variations between organisations, the interviews identified a series of common problems including:
- Decision makers in health care often cannot access the right type of analytical skills.
- In some cases there are too few analysts, in others they are too busy working on mundane data manipulation “shifting and lifting”.
- Where we do have analysts, their skills can be limited and they work in small units with little chance to develop professionally.
- The increasing amounts we rightly spend on information are not being matched by our investment in people to analyses the data we have.
Analysts do not form a homogenous occupational group but span many different disciplines and skills. The interviews suggested that the critical attributes are for people who are able to: (a) understand and structure the problems of managers/ clinicians (b) access evidence and information that was relevant to a problem (c) apply appropriate and robust methods to manipulate information and data and (d) communicate the findings accurately and clearly.
The reasons behind the shortfall in analytical capabilities encompassed issues covering the supply of analysis and training and support as well as the demand for their skills
Discussion In a situation where the problems are multifaceted, the solutions seem to be long term strategies that encompass: (a) Promoting ways that analyst can use networks to share and learn (b) Working at scale to overcome the problems of fragmented communities of analysts and the need to an array of different boundaries. (c) Supporting professional development and vocational training. (d) Supporting tools for analysis (e) Creating environments for innovation and (f) Develop new relationship with the experts. There are also important elements of cultivating demand for high quality analysis and to reinforce the value of analytics at a local level. The might include (a) ways to help roving business benefit (b) developing tools for auditing analytical development (c) Awareness raising and training with existing management development initiatives (d) often innovation in analytical methods could be driven by requirements from the centre.
Conclusions One of the most effective ways to improve the use of information to support healthcare is to invest in the capabilities of those analysts working directly in the service. This has implications for training and development programmes as well as the wider supporting structures than enable the development and implementation of new analytical methods.
Abstract no. 140 Obesity and cancer together impact upon survival (OCTOPUS) consortium ‘cancer e-lab’: a federation meta-analysis of trial data
Andrew Renehan, Farr Institute@HeRC, University of Manchester, Manchester
Emma Crosbie, Division of Molecular & Clinical Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester
Matthew Sperrin, Ruth Norris, Georgina Moulton, and Iain Buchan, Farr Institute, MRC Health eResearch Centre (HeRC), Division of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester
Richard Riley, Research Institute for Primary Care and Health Sciences, Keele University, Staffordshire
Richard Wilson, Centre for Cancer Research and Cell Biology, Queen’s University Belfast, Belfast
Introduction We seek to establish a ‘Cancer e-lab’ to address a complex clinical question regarding excess body weight (commonly expressed as elevated body mass index, BMI) and survival after cancer treatment. We will test this hypothesis in two federation meta-analyses, undertaking secondary analyses of trial data in patients in the non-metastatic disease settings of colorectal (CRC) and endometrial (EC) cancers. This project is funded by the World Cancer Research Fund (2016 to 2018). Elevated BMI is an established risk factor for several cancers. By extension, elevated BMI at or after cancer diagnosis might be associated with a poor prognosis, and indeed, many studies show such associations. This is a key rationale for weight management strategies in cancer survivorship. However, interpretation of analyses in the cancer post-diagnosis setting is susceptible to biases, including: treatment allocation, stage misclassification, reverse causality, and dose-capping chemotherapy in obese patients.
We argue that the optimum setting to test these associations is a secondary analyses of trial data, where stage, treatment and cancer endpoints are governed by protocol. Here, in a novel way, we extend this idem to meta-analyse data across many trials, thus increasing estimate precision, and allowing sufficient numbers to test for specificity of association.
Methods We will establish a Cancer e-Lab similar to the already established STELAR Asthma e-lab.1 This serves as “a data repository populated with a unified dataset from well-defined cohorts, which also provides the computational resources and a scientific social network to support timely, collaborative research across the consortium”. For OCTOPUS, the primary endpoint will be cancer-specific survival. We have identified 30 eligible RCTs for CRC and 6 RCTs for EC. Trial leads have been identified, contacted and pledged willingness to partake in the consortium. The federated meta-analysis approach means that data can stay at source and analysis comes to the data.
Results The e-lab will serve as a ‘data safe-haven’: “a repository in which useful but potentially sensitive data may be kept securely under governance and informatics systems that are fit-for-purpose and appropriately tailored to the nature of the data being maintained, and may be accessed and utilized by legitimate users”.2 Statistical methods for meta-analysis that preserve the clustering of patients within studies will be preferred. One-step hierarchical models with random effects will be explored as these have the advantage that one can implement non-linear trends and non-parametric flexible models. This approach can be computationally intensive.
Discussion & Conclusions The findings from this robust analytical platform will offer a clear direction whether or not there is an adverse effect of elevated BMI (compared with normal weight) on cancer-specific survival in CRC and EC, and inform weight management studies in survivors of these two common cancers.
References
Custovic A, Ainsworth J, Arshad H, Bishop C, Buchan I, et al. (2015). Thorax 70: 799-801.
Burton PR, Murtagh MJ, Boyd A, Williams JB, Dove ES, et al. (2015). Bioinformatics (Oxford, England) 31: 3241-3248.
Abstract no. 142 Towards quality health data: defining the health data pyramid
Jessica E. Lockery, Taya A. Collyer, and John J. McNeil, Monash University, Department of Epidemiology and Preventive Medicine, Melbourne
Introduction Health data is information about the physical, mental and social condition of a person stored in a form compatible with computer input, storage and processing for output as usable information. This definition is broad and includes data of varying structures and provenance. Evidence-based medicine is driven by a hierarchy of evidence represented in the seminal Evidence Pyramid1. As the prominence of ‘big data’ in health increases, it is becoming apparent that a similar hierarchy exists for health data. Developing a framework for assessing the quality of health data is crucial as the digital capability of medicine continues to evolve. To assist health professionals and researchers consider and discuss the quality of their health data, the development of a Health Data Pyramid is proposed.
Methods Key areas of concern were identified in the literature. Risk of bias, clinical accuracy, and burden of unstructured data cleaning were recognised as major causes of lower quality patient data.2,3 Classes of health data were assessed and ranked according to their likely clinical accuracy and the utility of the data structure for analysis.
Results Six major classes of health data were identified, ranked and assembled into a pyramid: Validated point-of-care (POC) data, POC data, Retrospective Clinical Data, Administrative Clinical Data, Administrative Non-Clinical data, and Unstructured Health Information. The top tier represents the highest quality patient health data. Assessment of the quality of population health data requires further consideration of data collection method (i.e. systematic vs opportunistic data collection) and data completeness. Consequently, population health data should be viewed through this lens.
Discussion Data science has the potential to aid the evolution of more adaptable, responsive health care and improve delays in translation. However, it is crucial that interpretation of health data is informed by data quality. Predictions of a bright future where each patient’s prognosis and optimal treatment are determined via machine learning strategies may be thwarted by the scarcity of high quality data,4 and the risks of relying on poor quality data are currently unknown. The proposed six-tiered Health Data Pyramid and lens for population data assessment offers a framework for health data quality bench-marking and serves as the basis for discussion about the fitness of certain types of health data for informing clinical practice, policy and planning.
Conclusion Development of the Health Data Pyramid is an important innovation for data quality assurance in medical care and research.
References
Murad MH, Asi N, Alsawas M, Alahdab F. New evidence pyramid. Evid Based Med. 2016 Aug21(4):125-7.
Goldman LE, Chu PW, Osmond D, Bindman A. Accuracy of do not resuscitate (DNR) in administrative data. Med Care Res Rev. 2013 Feb70(1):98-112
Hong Y, Sebastianski M, Makowsky M, Tsuyuki R, McMurtry MS. Administrative data are not sensitive for the detection of peripheral artery disease in the community. Vasc Med. 2016 Aug21(4):331-6.
Angus DC. Fusing Randomized Trials With Big Data: The Key to Self-learning Health Care Systems? JAMA. 2015 Aug 25314(8):767-8. doi: 10.1001/jama.2015.7762.
Abstract no. 143 A secured architecture enabling to link clinical information system with consumer health mobile applications
Frederic Ehrler, Cyrille Duret, Thibaud Collin, and Christian Lovis, University Hospitals of Geneva, Geneva
Introduction Mobile applications create new opportunities to deliver patient centred services. In order to provide services integrated with the hospital ecosystem, connection between smartphone applications and clinical information system (CIS) must be set up. The link with smartphone applications opens new doors that can be exploited for malicious purposes such as unauthorised access, use, disclosure, disruption, modification or destruction. In this poster, we propose a new architecture that can be implemented in order to minimise the risk of linking mobile application services to a CIS.
Challenges We present below the most severe attacks when connecting personal device to a healthcare information infrastructure. Denial of Service (DoS): The risk of denied access to a service due to bombardment by useless traffic is emphasised by the large number of connections.
Injection Allow the execution of malicious data and gives control to the whole system to hacker. Identity theft: If an unauthorised access to app services is damageable, the theft of the CIS identity would be a much more serious issue.
Proposed architecture In our architecture the mobile application communicates with the CIS through a server isolated in a specific area of the hospital infrastructure. The communications from the outside go through a reverse proxy controlling that requests are not obvious attacks. The isolated server cannot communicate directly to the server inside the secured zone to prevent the spread of an attack. The communications between the secured server and the isolated one rely on a polling technique. At a chosen frequency, the secured server sends a request to the isolated server to know whether the latter has a pending request (figure 3). In case a valid request is done, it is transferred into the secured zone. There, the identity of the emitter is consolidated with the internal identity using a mapping table. Then the request is escalated to the relevant service of the CIS. Finally, the response goes back up till the isolated server. Resistance to DoS: As the exposed server is physically isolated of the rest of the infrastructure, a DoS attack will be restrained to spread on other services. Resistance to injection: If the injection is not stopped by the reverse proxy, the unidirectional communication between the intra-server and the isolated server ensures that injections won’t be carried further into the system. Protection against CIS identity steal: Although app password is vulnerable to techniques such as phishing, the exposed information remains limited since the password accessing medical information is uniquely accessible inside the secured zone of our architecture.
Discussion We propose a novel architecture designed to reduce the risk of external attacks and little constraining for users. This is a first step toward a robust architecture connecting mobile applications and CIS. A formal validation through penetration testing and scalability validation will be required before using it in real settings.
Abstract no. 148 Automatic detection of nursing activities in home care with portable and ambient sensors
Dominik Wolff, Stefan Franz, Marianne Behrends, Jörn Krückeberg, and Thomas Kupka, Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig - Institute of Technology and Hannover Medical School, Hannover
Jonas Schwartze, Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig - Institute of Technology and Hannover Medical School, Braunschweig
Michael Marschollek, Medizinische Hochschule Hannover, Hannover
Introduction Demographic change leads to an increase of people in need of care. Most are elders cared by relatives at home. These informal caregivers often face this situation surprisingly and without any prior care know-how. The aim of the project “Mobile Care Backup” is to support informal caregivers managing their everyday tasks by applying technology. In particular, the aim is proactive provision of necessary knowledge and information for informal caregivers. Furthermore, the information provided should be adapted to the caregiver’s specific situation. The majority of knowledge units will address nursing activities, giving the informal caregivers a better understanding of their tasks. To provide this information, the automatic detection of nursing activities is necessary. Thereby an automatic care diary can be realised, too. This paper describes a concept for automatic detection of nursing activities in home care.
Methods Typically, specific nursing activities are connected to different fixed places inside the cared person’s home. Therefore the position of the caregiver is an important predictor of the activity performed. Thus, indoor positioning is a necessary application. Since position measurement alone is not sufficient for the detection of nursing activities, an abstraction of accelerometer and gyroscope data could be used additionally. Furthermore, many nursing activities, like the administration of pharmaceuticals, are executed at a specific moment or time frame of the day. Thus, dates of execution could predict activities, too. Data points from indoor positioning could be validated by logical rules. If the caregiver remains in front of the tub in the bathroom he is probably bathing his relative. Accelerometer and gyroscope data could be interpreted by a soft computing method, like an artificial neural network or support vector machine. The mentioned date of execution of a nursing activity could be aligned with previously entered daily routines. In a second step, a pattern classification approach will be applied, using the results of these three evaluations as feature vector, putting out one final nursing activity.
Result For the indoor positioning, GPS or beacon technology could be used. Accelerometer and gyroscope data could be retrieved from a smartphone or smartwatch, which could measure the date of execution, too. As different studies have shown, it is possible to abstract accelerometer data to activities of daily living. For this task, neural networks seem to suit better than support vector machines and other techniques.1
Discussion Indoor positioning using beacons should be more precise than GPS. The smartwatch’s accelerometer data is more significant than the smartphone’s. By merging these two approaches with the execution dates, we should reach a high classification rate. Camera-based approaches could produce good results as well, but will not be used due to acceptance issues. Information could be displayed on the smartwatch, so the caregiver is empty-handed to perform the activity.
Conclusion A suitable setup was found. The proposed system should produce good results. The next step is the system’s implementation and evaluation.
Reference
Preece SJ, et al. Activity identification using body-mounted sensors--a review of classification techniques. Physiological Measurement. 200930:R1-33.
Abstract no. 158 Exploring the extent to which prescribing and dispensing dose instructions differ
Clifford Nangle, Jackie Caldwell, and Marion Bennie, NHS National Services Scotland, Edinburgh
Stuart McTaggart, University of Dundee, Dundee
Introduction The Prescribing Information System (PIS) data mart, hosted by NHS National Service Scotland receives around 90 million electronic prescription messages per year from GP practices and around 75 million electronic dispensing messages per year from pharmacies across Scotland. Prescription messages contain information held in the GP10 prescription form (e.g. drug name, quantity and strength) stored as coded, machine readable, data while the prescription dose instruction consists of unstructured free text and is difficult to interpret and analyse in volume. Dispensing messages contain information imported from the prescription message and may be modified by the pharmacist during the dispensing process due to switching of pharmaceutical product to a generic equivalent and rephrasing of the dose instruction text for the patient. The aim is to perform a comparison of prescription dose instructions with dispensing dose instructions by extracting drug dose, unit and frequency information using Natural Language Processing (NLP) methods and to apply this comparison to drugs used in the treatment of depression and type 2 diabetes, namely antidepressants and antidiabetic drugs found in sections 4.3 and 6.1.2 respectively, of the British National Formulary (BNF).
Methods An NLP algorithm has been developed to extract drug dose amount, unit and timing frequency information from prescription dose instructions (This algorithm has a read rate of 97.5% and an error rate of 2.6% when processing dose instructions for drugs found in chapters 1 to 10 of the BNF). Prescription and dispensing messages will be linked using information present in both. The NLP algorithm will parse the dose instruction text and convert drug dosage information into a structured machine-readable form to perform the comparison. Accuracy estimates will be obtained by randomly sampling source records and performing a manual comparison.
Results The results of this study will be presented in the poster and will include a breakdown of how the prescription dose instructions have been altered during the dispensing process taking into account switching of pharmaceutical product.
Conclusions The analysis will be used to determine whether, or the extent to which, information held in the electronic dispensing messages is consistent with information held in the electronic prescribing messages that are stored in the PIS data mart.
Abstract no. 174 Screening women in Glasgow: comparing uptake across breast, cervical and bowel cancer screening at an individual patient level
Paula McSkimming, Richard Papworth, Alex McConnachie, and Colin McCowan, Robertson Centre for Biostatistics, Institute of Health & Wellbeing, University of Glasgow, Glasgow
Katie Robb and Marie Kotzur, Institute of Health & Wellbeing, University of Glasgow, Glasgow
Introduction Screening can reduce deaths from cervical, bowel and breast cancer if the people invited participate, however screening uptake among Scottish women is 73% for breast, 69% for cervical but only 61% for bowel. Little research has examined why bowel screening fails to achieve the uptake rates of breast and cervical. The availability of Glasgow-wide data for the complete population within a socioeconomically diverse region with comparatively low screening uptake provides a unique context for this research. To determine why women who are eligible for all three types of screening choose to do none, some or all tests and to shed new light on barriers unique to bowel screening we will investigate demographic and medical factors associated with the lower participation in bowel screening relative to breast and cervical screening.
Methods Data on screening invitations and attendances for women aged 20-74 in the NHS Greater Glasgow and Clyde Health Board who were sent an invitation or attended at least one of the three programmes during 2009-2013 were linked to demographic data, hospital discharge records, GP Local Enhanced Service (LES) data and death certification records. The number of attendances for breast and cervical screening and the number of bowel screening tests returned were recorded. It was not possible from the data provided to identify the number of invites to cervical screening or the relationship between a cycle of invites and attendances for all three programmes. Co-morbidity was assessed using a Charlson Index based on hospital records and GP LES data and socio-economic status was categorised by Scottish Index of Multiple Deprivation (SIMD) quintile based on home postcode. Logistic regression for each screening programme assessed the association of age, SIMD, Charlson Score and other factors on screening participation. Women who were invited to participate in all three programmes were also identified with similar analysis performed.
Results There were 430,591 women invited to take part in at least one of the screening programmes over 2009-2013. 116,212 (72.6%) women attended for breast screening out of 159,993 invited over the period, 250,056 (80.7%) women from 309,899 attended cervical screening and 111,235 (61.7%) women completed bowel screening from 180,408 invited. There were 68,324 women who were invited to participate in all three screening programmes during the study period with 35,595 (52.1%) participating in all 3 programmes.
Discussion Despite having rich data from the individual screening programmes, allowing unique insight into cancer screening uptake, recording of patient invitations and attendances differed significantly between the programmes. This provided limitations to the analyses such as identifying the number of invitations prior to uptake and screening cycle adherence. Women have lower participation in bowel screening than for breast or cervical, although the same demographic factors are associated with participation. Only half of women eligible for all three screening programmes participate in them all.
Conclusion Older women and those living in more affluent areas were more likely to attend for breast, cervical and bowel screening. Women with multi-morbid illness were less likely to participate in all screening programmes.
Abstract no. 176 Big data platform for comparing data-driven pathways for warning potential complications in patients with diabetes
Jose Ramón Pardo-Mas, and Carlos Sáez, Instituto de Investigación Sanitaria La Fe IISLAFE, Valencia
Salvador Tortajada and Juan M Garcia-Gomez, Instituto Universitario de Tecnologías de la Información y Comunicaciones (ITACA), Universitat Politècninca de València, Valencia
Bernardo Valdivieso, Universitat Politècnica de València, Valencia
The use of Big Data platforms in health care is in an uprising trend. Big Data technologies allow easier and faster analysis of vast amounts of data such as patient pathways, which may lead to better decision-making. We present a Big Data methodology approach for warning potential complications in patients with diabetes by finding local similarities among their patient pathways. Specifically we present a Storm based platform that implements our extended version of the Smith-Waterman algorithm to detect clinical complications in diabetic patients by comparing them to a whole set of Electronic Health Records (EHR). A demo of the system is available at www.lppaalgorithms.com.
The extended version of Smith-Waterman compares the patients based on a tuple form of clinical records, known as Patient Pathway (PP). Data processing was made to obtain the PP dataset from the EHRs, where each one is composed by the clinical observations ordered in time. We define five different types of clinical observations: hospitalisation, outpatient consultations, emergency room visits and laboratory tests for glucose and creatinine. The episodes of a patient are codified and put together in the PP following the timeline of each episode. Then, using the SW-based algorithm a comparison between each pair of PPs is carried out. The comparison has a possible output: cardiopathy complications. In case the PP pair is not ranked, it is not shown.
The logic for comparing PP is developed using a Big Data framework called Apache Storm. Different components are defined: a Spout that gathers the tuples from a web queue and passes the data to the logic, and a set of Bolts, each Bolt has a unique function inside the topology, and the Bolts can be largely replicated. These Bolts work together to join the pathways from the web queue with each one on the database, creating a set of 2 tuples for each database entry: the query patient, and the database patient. After finding local similarities, it is possible to rank the 10 best PP alignments. These are sent as possible outcomes for the query patient.
The LPPA system is based on different technologies. The web interface is primarily programmed using HTML and JavaScript, having WordPress as a design tool. The server is programmed in JavaScript, using node.JS to run the system, and various libraries, mainly Socket.io, and RedisIO.js. The database is implemented using Redis, a non-SQL on memory database that allows the PUB-SUB queues. These are used as channels, and the Storm framework subscribes to an input channel and publishes to the patient output channel. The client can subscribe this channel to retrieve the information in Real-Time.
This approach showed a precision of 0.26, a recall of 0.95 and a 0.42 F-score. This development is a first approach of PP predictive use in health. Despite having good recall results there is still improvement merging for other measures. There is still much work to be done.
Abstract no. 178 A systematic root cause analysis into the increase in escherichia coli bacteraemia in Wales over the last 10 years
Jiao Song and Ronan Lyons, Farr institute, Swansea university, Swansea
Angharad Walters, Ashley Akbari, Martin Heaven, and Damon Berridge, Farr institute, Swansea University, Swansea
Margaret Heginbothom and Julie Arnott, Public Health Wales, Cardiff
Introduction Bacteraemia is of public health importance due to the high morbidity and mortality associated with this condition. Numbers and rates of E. coli bacteraemia in Wales have risen substantially over the last 10 years and it is clear that interventions aimed at preventing the spread of E. coli and the development of bacteraemia need to be introduced to interrupt this upward trend. Public Health Wales have been requested to undertake an investigation into the rise of E. coli bacteraemia by the Chief Medical Officer for Wales. Anonymised, routinely collected administrative data stored in the Secure Anonymised Information Linkage (SAIL) databank will be used to provide descriptive and risk factor analysis.
Methods Anonymised blood microbiology culture data reported between 2005 and 2011 are included in the SAIL databank. E. coli bacteraemia cases have been linked with Welsh demographic service (WDS) data to obtain address information, week of birth and gender. All potential controls are randomly selected from WDS. Three different methods were used to identify controls: 1) the cases and controls had a Welsh address on the date the E. coli blood sample of the case was received (reference date), and both cases and controls lived in Wales during the 91 days before the reference date and controls did not have an E. coli blood culture sample during the 91 days prior 2) Method one was extended to also match on age and sex 3) method 2 was extended by additionally matching on GP practice. All cases and controls in these three groups have been linked with the patient episode database for Wales (PEDW), Welsh general practice data, emergency department dataset, outpatient data and Welsh index of multiple deprivation (WIMD) to flag the relative risk factors.
Results Logistic regression and conditional logistic regression modelling techniques have been used to identify risk factors for developing E. coli bacteraemia. All three models show that kidney infection, urine infection, likely hospital antibiotics prescription and high comorbidity score are the risk factors with the highest odds ratios. For group 1, the odds of a patient with a high comorbidity score are 16 times the odds of a patient with a low comorbidity score. The odds of a patient who had likely antibiotics prescription from hospital within 3 months are 16.5 times the odds of a patient who did not. The odds of a patient who had an urine infection within 3 months are 21.5 times the odds of a patient who did not. The odds of a patient who had kidney infection within 3 months are 145.7 times the odds of a patient who did not.
Conclusions Determining the factors associated with the development of E. coli bacteraemia will allow patients at highest risk to be identified. If these risk factors are modifiable, then preventive interventions can be introduced to reduce the number of potential cases of E. coli bacteraemia.
Abstract no. 186 Predictive modeling with machine learning based variable selection: a study on 30-day readmission prediction
Nan Liu, Kheng Hock Lee, and Marcus Eng Hock Ong, Singapore General Hospital, Singapore
Lian Leng Low and Julian Thumboo, Duke-NUS Medical School, Singapore
Introduction Healthcare resources are finite and frequent readmissions can overwhelm even developed health systems. Patients with frequent admissions also experience significant psychological stress and financial burden. In the United States, 30-day readmissions are considered an accountability measure and quality indicator. In this study, we aimed to develop a predictive model to assess the risk of 30-day readmissions for admitted patients, where machine learning (ML) algorithms were used for clinical variable selection.
Method This was a retrospective observational study in Singapore General Hospital. All adult patients ≥21 years were included if they had alive-discharge episodes from Department of Internal Medicine in 2012. Patients who died during the index admission, non-residents, or who had a discharge destination other than home at discharge were excluded. Comorbidities were identified using ICD-10 codes in any primary or secondary diagnosis fields dating back to one year preceding the index admission. Charlson comorbidity index (CCI) and the LACE score were computed for each patient. Additionally, patient demographics and laboratory variables were extracted. We used 80% randomly selected data for model derivation and the remaining 20% for model validation. A novel machine learning based variable selection algorithm was proposed to determine a subset of strong independent predictors. The novel variable selection method was developed based on ensemble learning framework and random forests.
Results 6,377 unique patients were admitted to internal medicine wards in 2012. 515 (8.1%) were excluded from analysis. Of the 5,862 (91.9%) patients remaining in the cohort, 572 patients (9.8%) were readmitted within 30 days after discharge. We built two predictive models: Model A used a full set of variables and Model B used selected variables obtained from our proposed algorithm. Model A achieved C-statistic of 0.68, sensitivity of 66.0% and specificity of 60.4% at the cut-off score of 90. Model B achieved C-statistic of 0.7, sensitivity of 70.1% and specificity of 60.4% at the cut-off score of 90.3. In comparison, the LACE score had lower C-statistic (0.62), sensitivity (66%) and specificity (52.7%) at the cut-off of 6.
Discussion By applying the novel variable selection algorithm, we selected a total of fifteen significant predictors, built and validated a predictive model. Both Model A and Model B outperformed the LACE score in predicting 30-day readmissions, which showed the evidence that machine learning based predictive model is a promising replacement of traditional clinical score. Moreover, with a similar cut-off (90 vs 90.3), Model B derived on selected variables achieved higher sensitivity (70.1% vs 66.0%) compared to Model A in which all variables were used. This has demonstrated that a subset of significant predictors is desired in building predictive models.
Conclusion We observed that a few selected predictors outperformed the full set in predicting the risk of 30-day readmissions. The novel machine learning based variable selection method has proven to be effective in choosing the most discriminatory predictors. Moving forward, we will conduct a large-scale validation study using the hospital’s electronic health records for all clinical departments.
Abstract no. 187 Facebook ‘Likes’ do not accurately predict symptom reports: a machine learning study
Chris Gibbons, University of Cambridge, Cambridge
Introduction Digital footprints, including Facebook ‘Likes’, have been used to successfully predict factors including personality, gender, and relationship status. We attempted to predict medical symptom reports using Facebook ‘Like’ information.
Methods Data were collected from the myPersonality application. 2036 participants submitted symptom data (collected using the Pennebaker Inventory of Limbic Languidness) alongside demographic information and Facebook ‘Like’ data. We trained multiple machine learning algorithms to predict symptom reporting using digital footprint information. Algorithms including generalised linear model with lasso regularisation (GLM), support vector machines with linear and polynomial basis function kernel (SVM linear and polynomial), regression trees (trees) and random forests with 500 trees (random forest). Data were randomly split into ‘training and ‘validation’ samples with an 80:20 ratio. Algorithm performance was compared using Pearson correlation between predicted and real values from the validation dataset. RMSE was used as a measure for prediction error.
Results 2036 adult participants (mean age = 25.14, SD = 8.51) provided information on all measures. The best algorithm for predicting the actual symptom reports using the full range of the scale was the random forest (r = 0.28, RMSE = 31.29). Regression with lasso regularization performed similarly well (r = 0.22, RMSE = 30.81). Regression trees (r = 0.08, RMSE = 30.68) and SVM (linear: r = 0.11, RMSE = 47.34 polynomial: r = 0.05, RMSE = 47.82) performed worse at predicting symptom reports using the information based on participants’ Facebook ‘Likes’.
Discussion & Conclusions Multiple machine learning algorithms failed to predict symptom reports with high accuracy. This result may be explained by lack of temporal sensitivity inherent in Facebook ‘Likes’ which, in contrast to symptom reports, provide a signal that remains consistent over time. Symptom reports may be better predicted by signals in digital footprint that fluctuate over time, such as online behaviour and status updates. The study highlights the importance of matching transient and enduring data, even when using powerful deep machine learning approaches.
Abstract no. 188 The features patients ‘want’ in a smartphone app to support asthma self-management and their clinical effectiveness: a systematic review of the telehealth interventions and online discussion forums
Chi Yan Hui, Tracy Jackson, Eleftheria Vasileiou, and Hilary Pinnock, Asthma UK Centre for Applied Research, Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh
Robert Walton, Centre for Primary Care and Public Health, Barts and The London School of Medicine and Dentistry, Blizard Institute, Queen Mary University of London, London
Brian McKinstry, Primary Care eHealth, Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh
Richard Parker, Health Services Research Unit, The University of Edinburgh, Edinburgh
Introduction Self-management with an action plan, as opposed to passive self-monitoring, improves health outcomes. Mobile technology, incorporating education, personalised asthma action plans and facilitating professional support, could be an option for supporting asthma self-management. Clinical research focusses on health-related outcomes whereas the eHealth market focusses on customer engagement. Therefore, we aimed to assess both the clinical effectiveness of the technology, and also identify application features that patient want and will continue to use in a self-management app.
Methods For clinical effectiveness, we followed Cochrane methodology to systematically review randomised controlled trials (RCTs) of telemedicine in adults/teenagers with asthma, and synthesised data on health outcomes (e.g. asthma control questionnaire and/or exacerbation rate). We searched nine databases and 2 reviewers selected eligible papers, extracted data, and used meta-analysis and narrative synthesis. For patients ‘want’ features, we systematically searched Google for ‘asthma’ ‘forums’ and retrieved posts in which patients discussed ‘wanted’ features. Eligible posts were assessed by two reviewers. We identified the frequency with which features were mentioned and synthesised the perceptions thematically.
Results We included 12 RCTs (published in 14 January 2000 –January 2015 updated search in April 2016.) in the systematic review. Meta-analysis (n=3) showed improved asthma control (mean difference -0.25 [95% CI, -0.37 to -0.12]). The effect on health outcomes of the 10 common features (education, monitoring and electronic diary, action plans, reminders or prompts to promote medication adherence, professional support for patients, raising patient awareness of asthma control, and supporting the healthcare professional) varied, but there were no examples of harm. No interventions explicitly reported the adoption of and adherence to the technology system by patients and healthcare professionals. From 25 online social forums, we included 22 posts (November 2013 –November 2015). 42 people with asthma commented on 44 application features, which we grouped into five categories (self-monitoring, feedbacks/advice, professional/carer support, reminders, and others e.g. stress management). Feelings ranged from ‘positive’, ‘appreciative but worried (e.g. about confidentiality)’, ‘nothing unique’, ‘doubtful’ and ‘negative’. The majority of comments about apps incorporating monitoring peak flows (sometimes with novel gadgets), symptoms and medication usage were positive, but without explicit mention of action plans. Smart gadgets, such as electronic inhaler logs provoked a wide range of responses.
Discussion The effect of telehealth applications, many including the features identified by patients, on health outcomes varied but was at least as good as traditional modes. People with asthma showed interest in logging health status with symptoms or peak flows, in contrast to the clinical evidence that evaluates self-management.
Conclusion Mobile technology is an option for supporting asthma self-management. The lack of discussion about action plans, suggests that today’s apps are limited to self-monitoring rather than self-management. Further research is needed to understand this limitation and the features associated with adoption and adherence to self-management.
Abstract no. 194 Moving WHO international classification of health interventions (ICHI) towards semantic interoperability
Jean Marie Rodrigues, INSERM LIMICS U1142 UPMC UP13 Université Jean monnet/Université de Lyon, Paris
Sukil Kim, Catholic University of Korea, Seoul
Béatrice Trombert-Paviot, INSERM U1142 LIMICS, Saint-Etienne & Paris
Introduction The WHO International Classification for Health Intervention (ICHI) is based on an ontology framework defined in ISO 1828, named Categorical Structure for terminological systems of surgical procedures. We reviewed 574 ICHI alpha 2016 existing codes and structure and compared with EN 1828 and the SNOMED CT (SCT) procedures hierarchy concept model. We conclude that modifications are needed to design a more semantically defined version of the ICHI chapter Medical and Surgical interventions. We checked if the three axes of ICHI (Target, Action and Means) are sufficient to express semantically the Medical and Surgical interventions and how ISO 1828 and SNOMED CT concept model for the Procedure Hierarchy express these interventions.
Method We studied 574 ICHI alpha 20161 interventions from three chapters: Nervous system, Ear, and Endocrine system. We compare the existing three axis structure of ICHI with ISO 18282 and SCT concept model.3
Results The different concept model of SCT attributes using the word “direct”, as in procedure site direct, direct morphology, direct device and direct substance are equivalent to ISO 1828 semantic link “hasObject” and semantic categories “Anatomical Entity”, “Lesion” and “Interventional equipment”. They have no equivalent in ICHI. Further on the attributes Procedure site indirect allows to provide the equivalent with ISO 1828 semantic link “hasSite” with the semantic category “Anatomical entity”. The ICHI axis Action should be duplicated to express the intent and the deed. The ICHI Target axis should be extended to pathology as adhesions or calculus, and to medical devices as pacemaker. The ICHI Target axis should be duplicate in Direct Target grouping the semantic categories on which the action is carried out and Indirect Target which is the site on which is localised the object on which the action is carried out as “Implantation of internal device, ventricles of brain”. The ICHI Means axis should be extended to medical devices and drugs.
References
International Classification of Health Intervention: Alpha2 Version 2016 http://mitel.dimi.uniud.it/ichi/docs/
EN ISO 1828 2012. http://www.iso.org/iso/iso_catalogue/catalogue_tc/catalogue_detail.htm?csnumber=52388
SNOMED CT® Editorial Guide July 2016 International Release (US English). https://confluence.ihtsdotools.org/display/DOCEG/SNOMED+CT+Editorial+Guide
Abstract no. 199 Enhancing nationwide medico-administrative databases analysis with SAF4Big, a statistical analysis framework for big data in healthcare
Alexandre Georges, Alexandre Caron, Jean-Baptiste Beuscart, Cécile Bonte, and Nicolas Girier, CHU Lille, Lille
Thibaut Balcaen, Emilie Baro, Jean-Baptiste Dugast, Fabien Bray, Grégoire Ficheur, and Emmanuel Chazard, Univ. Lille, EA2694, Lille
Many epidemiological studies now rely on the reuse of large medico-administrative databases. In those studies, most of the time is consumed in managing data and performing basic statistical analyses, and is not available anymore for complex statistical and medical analysis, so that the potential of such databases is sometimes under exploited. The objective of this work is to build SAF4Big, a statistical analysis framework for big data in healthcare, using literature-based specifications. A literature review was performed on PubMed in 4 different medical domains: caesarean deliveries, cholecystectomies, left implantable ventricular assist devices, and hip replacement surgeries. We identified 43 papers relating analyses of large databases. They reported epidemiological indicators (e.g. mean age) that were abstracted to features (e.g. univariate description of a quantitative variable), that were implemented through 37 new functions in R programming language (e.g. a function will draw a histogram, compute the mean with confidence interval, quantiles, etc.): 4 functions for data management, 9 for univariate analysis, 8 for bivariate analysis, 11 for multivariate analysis, and 5 intermediate functions. Those functions were successfully used to analyse a French database of 250 million discharge summaries. The set of R ready-to-use functions defined in this work could enable to secure repetitive tasks, and to refocus efforts on expert analysis.
Abstract no. 202 Release of the standard export data format by the japanese circulation society for standardized structured medical information exchange extended storage
Masaharu Nakayama and on behalf of the IHE-J Cardiology Team, Tohoku University, Sendai
Background In the era of big data, utilization and analysis of large amounts of clinical data are important. A substantial clinical database for cardiovascular disease requires a wide range of data including medical records, medication, laboratory tests, physiological examinations, and data from multiple modalities. Although these data are in a digital format, data transition from the hospital information system (HIS) to the database is performed manually in most hospitals, resulting in excess burden for physicians and clinical research coordinators. Hence, automated transfer of these data from the HIS to the database is desired, which requires the determination of standard formats for data connection between the HISs and the database.
Methods and Results In Japan, the standardized structured medical information exchange (SS-MIX) was developed in 2006 as a standard data storage format to share clinical data from various vendor-derived HISs and was revised (SS-MIX2) in 2012. Several national database projects and local electronic health records use the SS-MIX2. Moreover, data are retrieved from the SS-MIX2 storage for secondary use. The SS-MIX2 storage is divided into two categories: standardized and extended storage. The standardized storage includes standard structured and coded clinical data, such as basic patient data, prescriptions, and laboratory data transferred from the HIS in Health Level Seven format. All other data in non-standardized formats, such as electrocardiogram (ECG), echocardiography (UCG), and catheter examination data, are stored in the extended storage. Standardized storages are often utilized for several projects however, extended ones are rarely used. In 2014, the Japanese Circulation Society (JCS) decided to develop structured standard formats to save clinical data in SS-MIX2 storage, in collaboration with an international association that aims to improve the use of computer systems in healthcare through standards, integration healthcare enterprise (IHE), IT vendors, and several academic cardiology societies. In 2015, the Standard Export datA forMAT (SEAMAT) for ECGs, UCGs, and catheter examination data was announced. According to SEAMAT, the item name, unit, and format for SS-MIX2 can be determined. This step would help to effectively establish a nationwide clinical database and reduce the tedious manual data input by clinicians and clinical research coordinators. However, hospitals may continue to incur significant costs to equip information systems with this format. Thus, a program that enables the conversion of comma-separated data from information systems to SEAMAT is also being developed, which will be a useful and economical tool for transferring huge clinical data to SS-MIX2.
Conclusions Sharing medical information among hospitals is crucial for patient care and research. In Japan, JCS developed SEAMAT for the secondary use of important clinical data in cardiology. These stepwise implementations are crucial to achieve a nationwide clinical database for cardiology disease in Japan.
Abstract no. 210 Actual (in)validity of scientometric analysis in online scientific world
Izet Masic, Faculty of Medicine, University of Sarajevo, Sarajevo, Bosnia and Herzegovina
Edin Begic, Faculty of Medicine, University of Tuzla, Sarajevo, Bosnia and Herzegovina
Introduction Citing is an integral part of an article. Scientometric indicators of work of an author are number of citations, H index, i10 index and g index. Scientometric analyses of the work of one author are essentially meaningless, since they are based on platform of Google Scholar, which indexes any document belonging to academic domain, and is accessible for manual manipulation of content, and in most cases takes into account the work of authors who belong to the contemporary digital world and do not take into account the work of one author at a time when the content was not so much accessible to online community.
Method This article aims to show the (in)validity of scientometric analysis in the online scientific world.
Results Scientometric indicators of Web of Science, Scopus and Google Scholar (including Publish and Perish software) were analyzed. Incorrect quotation of an article or book lead to two different quotations of one article with different number of citations (which ultimately leads to incorrect H or i10 index). Digitalization of content in the modern age skipped numerous citations prior to digitization, and information on Google Scholar can give a false picture of the author (especially for authors who do not belong in the modern era). By analyzing the work of one author, we came to the conclusion that the Web of Science is more selective than Scopus and Google Scholar (Google Scholar shows the highest number of total papers, as well as the total number of citations). Scopus is more selective database than Google Scholar, but it is not known whether it is more valid. H index displayed on Scopus page is always lower than the index of H Google Scholar.
Discussion There is a lot of uncertainty in scientometric analysis of one author (in scientific community ranking in some field is an important factor). Google Scholar is subject of numerous manipulations, because it collects information, without paying attention on the credibility of the results, which leaves a lot of space for doubts in its validity.
Conclusion Scientometric analysis of the work of one author has more space for progress and the new tools are necessary for better evaluation. ORCID ID is a necessary thing for identification of an author in the online world.
Abstract no. 232 Design for governing the flow of data in a complex, multi-stakeholder and multi-jurisdictional health informatics project across canada
Karim Keshavjee, Frank Sullivan, Michelle Greiver, and Don Willison, University of Toronto, Toronto
Introduction The incidence of diabetes continues to grow in Canada and across the world. Diabetes Action Canada (DAC) has been funded by the Canadian Institutes of Health Research and others to conduct observational and interventional human studies with the goal of predicting and preventing diabetes complications. DAC brings together key stakeholders from across Canada. While there is a critical need to share information towards common goals, each stakeholder group has its own risks and concerns to address to meet the needs of its constituents. For example, ethical decision-making for research in Indigenous peoples is governed by a framework reflecting the requirements of First Nations. Legal frameworks in Quebec are different from those in Alberta. Cultural norms are different in British Columbia than in Alberta. The aim of this project is to address the various barriers to information sharing to enable data flow that will support the goals of DAC.
Method The UK Design Council’s Double-Diamond (Discover, Define, Develop, Deliver) method was used to identify key issues that each stakeholder feel they need to manage for appropriate governance (Discover). Key functionalities were required to manage the issues and achieve clinical and workflow goals. An iterative approach was used to engage a series of stakeholders to develop a solution that would work for all stakeholders. We also used a reflective process to identify salient features of governance processes of previous successful projects that could inform the development of our solution.
Results We have developed a novel Governance “Microbubble” model that incorporates several desiderata of interoperability: 1) Standard operating procedures and approaches to governing high risk, high stakes health or research goals 2) Standardized operationalization of generally accepted Governance Principles (transparency, accountability, etc.) 3) Standardized policies and procedures that are intended to engender trust, trustworthiness and confidence in data sharing 4) Oversight over work-flows, information flows and information technology to ensure that outcomes are consistent with goals and that risks are being managed appropriately 5) Scalability as new entities participate in the data sharing process. The model has been reviewed with key stakeholders it has or is attaining face validity and is currently being operationalized.
Discussion Multi-jurisdictional and multi-stakeholder projects face significant barriers to information sharing. Overcoming these barriers requires innovative social processes as much as they require innovative technologies.
Conclusion We have developed a conceptual governance, workflow, information flow and information technology oversight ‘package’ that can be standardized for use by multiple stakeholders in multiple-jurisdictions that have a plurality of ethical and regulatory frameworks. Operationalization and experience with the conceptual model will be reported at future conferences.
Abstract no. 239 EyeDraw Pedigrees: a case study in applying user-centred design to open source, clinical software development
Maria Cross and Jugnoo Rahi, University College London, London
George Aylward, Moorfields Eye Hospital, London
Introduction With the increasing role of genetics in modern healthcare, pedigree-drawing is becoming a vital part of medical history-taking. As we move toward a universal adoption of electronic medical records, there is an increasing demand for pedigree-drawing software appropriate for use within patient consultations. However, there is a paucity of freely available, interactive drawing tools that meet the needs of clinical users.
Methods We adopt a user-centred framework to develop a clinical pedigree-drawing application intended to be both useful and usable in clinical contexts. A mix of methods was employed, including semi-structured interviews, user goal and task analysis, and usability testing. Participants (two consultant ophthalmologists, one consultant clinical electrophysiologist, one genetic counsellor, one clinical lecturer, and two postgraduate research students) were identified as “expert users” working within clinical paediatric ophthalmic genetics. To develop the tool, we contributed to an open source JavaScript-based drawing software, EyeDraw, from the OpenEyes Foundation. Following an agile method, software modifications were made iteratively according to user feedback. Final testing, completed by two authors, involved drawing clinical pedigrees from the Great Ormond Street Hospital paediatric ophthalmology department. Pedigrees were identified from paper notes during a retrospective medical record review of patients seen in the outpatient clinic during a two-week period in October 2016.
Results Requirements elicitation defined the need for a pedigree drawing tool that was interactive, worked across a range of devices and was intuitive, following standardised pedigree drawing notation as recommended by the National Society of Genetic Counsellors. A decision to develop a web-based application was made to allow for cross-platform usage. The iterative development process took five months, employing both face-to-face and remote use engagement. Development ceased when no more user recommendations were made. Final software testing included 48 pedigrees (median 7 family members, range: 3-33; median 2 generations, range: 2-5). All tested pedigrees could be drawn, visualising all the data observed in the parallel paper-based approach including consanguineous families, multiple mates, multiple diagnoses and individual level information such as age and genotypic data.
Discussion User feedback and software tests presented in this case study demonstrate a successful user-centred approach to developing clinical software. As web-based, open source software, the resulting pedigree-drawing tool is suitable as a standalone application or could be integrated into any other web-based system. However, while cases seen at the Great Ormond Street paediatric ophthalmology department are complex, development and testing was limited to this context further work is required to assess the suitability of the software for other potential uses including different medical specialties and research.
Conclusion With the involvement of expert users throughout the process, we have developed and tested an open source electronic pedigree-drawing tool. The tool is suited to interactively draw the small, but potentially complex pedigrees encountered during clinical consultations.
Abstract no. 258 Reuse of electronic health record data in clinical decision support systems
Georgy Kopanitsa, Tomsk Polytechnic University, Tomsk
The efficiency and acceptance of clinical decision support systems (CDSS) can increase if they reuse medical data captured during health care delivery. High heterogeneity of the existing legacy data formats has become the main barrier for the reuse of data. Thus, we need to apply data modeling mechanisms that provide standardization, transformation, accumulation and querying medical data to allow its reuse. In this paper, we focus on the interoperability issues of the hospital information systems (HIS) and CDSS data integration. The project results have proven that archetype based technologies are mature enough to be applied in routine operations that require extraction, transformation, loading and querying medical data from heterogeneous EHR systems. Inference models in clinical research and CDSS can benefit from this by defining queries to a valid dataset with known structure and constraints.
Abstract no. 261 Automated pdf highlights for faster curation of neuordegenerative disorder literature
Honghan Wu, Anika Oellrich, and Richard Dobson, MRC Social, Genetic & Developmental Psychiatry Centre (SGDP), King’s College London, London
Christine Girges and Bernard de Bono, Farr Institute of Health Informatics Research, UCL Institute of Health Informatics, University College London, London
Tim Hubbard, Department of Medical & Molecular Genetics, King’s College London, London
Introduction Alzheimer’s disease, the most common neurodegenerative disorder, is expected to cause 1 million new sufferers per year as early as 2050. Similarly, numbers of people suffering from Parkinson’s disease are expected to rise to between 8.7 and 9.3 million by 2030, making it the second most common neurodegenerative disorder. As such, these diseases have become a major focus of global biomedical research in an effort to develop a detailed understanding of causes and pathology that will lead to novel treatments and improved care. The ApiNATOMY project (http://apinatomy.org/home) aims to contribute to our understanding of neurodegenerative disorders by manually curating and abstracting data from the vast body of literature amassed for these illnesses. As curation is labour-intensive, automated methods are sought that allow for faster manual curation. Here we present our method aimed to learn the highlighting behaviours of human curators.
Methods PDFs are converted into sentence-separated XML files using Partridge. Sentences potentially relevant for the curator, are identified through an algorithm that assesses each sentence individually and scores its relevance based on linguistic (cardinal numbers preceding nouns, characteristic subject-predicate pairs), semantic (named entities) and spatial features (splitting of papers into regions and section assignment). Linguistic and semantic features were weighted based on the percentage of their occurrences in highlighted sentences and spatial regions. The overall score of a sentence was calculated using a linear function that combined scores of all identified features in it. The parameters of the linear function were chosen from the values that led to best performance on the training data. To configure and evaluate the algorithm, we used PDF files that had been manually assessed and highlighted (by one curator) as part of the ApiNATOMY project. The data is divided into two sets: configuration (183 papers) and evaluation (58 papers). We also implemented four binary classifiers (Perceptron, Passive Aggressive Classifier, kNN and Random Forest ) based on the bag-of-words model as baselines.
Results Using a test set manually corrected for tool imprecision, experiments showed that our approach achieved a macro-averaged F1-measure (a measure of a test’s accuracy) of 0.51, which was an increase of 132% compared to the best baseline model - Perceptron. In addition, a user based evaluation by a senior curator (author CG) was also conducted to assess the usefulness of the methodology on 40 unseen publications, which reveals that in 85% cases all highlighted sentences are relevant to the curation task and in about 65% of the cases, the highlights are sufficient to support knowledge curation task without the need to consult the full text.
Conclusion Our initial results are encouraging and we believe that the results presented are a promising first step to automatically preparing PDF documents to speed up the curation process and consequently lower costs in projects such as ApiNATOMY.
Abstract no. 265 Machine learning analysis substantiates importance of inter-individual genetic variability in PPAR signalling for brain connectivity in preterm infants
Michelle Krishnan, Paul Aljabar, Zi Wang, Gareth Ball, Serena Counsell, Giovanni Montana, and David Edwards, King’s College London, London
Ghazala Mirza, University College London, London
Alka Saxena, Guy’s and St Thomas’ NHS Foundation Trust, London
Introduction The incidence of preterm birth is increasing, with a high proportion of survivors experiencing motor, cognitive and psychiatric sequelae. Prematurity places newborn infants in an adverse environment accentuating their individual ability to cope with systemic challenges, and calls for precision in healthcare interventions. Machine learning strategies are used here to investigate the neurobiological consequences of prematurity. Given the establishment of a large genetic contribution to quantitative neuroimaging features informative of downstream function, and the assumption that a subset of genetic markers will be found in statistically meaningful association with a subset of image features, computational models must be able to select those informative variables. Multivariate sparse regression models such as the sparse Reduced Rank Regression method (sRRR) obviate the need for multiple-testing correction and significance thresholds, since this involves fitting a predictive model using all SNPs and ranking them based on their association to the image features (Vounou et al., 2010, Vounou et al., 2012).
Method 272 infants (mean gestational age (GA) 29+4 weeks) had magnetic resonance (MR) imaging at term-equivalent age (mean post-menstrual age (PMA) 42+4 weeks). 3-Tesla magnetic resonance images were used for probabilistic tractography (Robinson et al., 2008), using a 90-node anatomical neonatal atlas (Shi et al., 2011) and custom neonatal registration pipeline (Ball et al., 2010). A weighted adjacency matrix of brain regions for each infant was converted into a single vector of edge weights based on fractional anisotropy (FA), resulting in one matrix of n individuals by q edges, where n = 272 and q = 4005, adjusted for major covariates (post-menstrual age at scan (PMA), gestational age at birth (GA)) and ancestry. Saliva samples were collected using Oragene DNA OG-250 kits, and genotyped on Illumina HumanOmniExpress-24 v1.1 chip. The genotype matrix was converted into minor allele counts, including only SNPs with MAF ≥5% and 100% genotyping rate (556 227 SNPs). sRRR model parameters: SNPs at each iteration (n = 500), stability selection with 1000 subsamples of size 2/3 subjects, convergence criterion = 1x10-6, resulting in a ranking of all genome-wide SNPs based on their importance in the model. A null distribution was computed by running sRRR in the same way, additionally permuting the order of subjects within the phenotype matrix between each subsample during stability selection with 20 000 subsamples.
Results sRRR detected a stable association between SNPs in the PPARγ gene and the imaging phenotype fully adjusted for GA, PMA and ancestry. SNPs in PPARγ were significantly over-represented among the variables with the uniformly highest ranking in the model, contributing to a broader significant enrichment of lipid-related genes among the top 100 ranked SNPs.
Discussion In concordance with findings from two previous independent studies of a comparable cohort (Krishnan et al., 2016, Boardman et al., 2014), these results suggest a consistent association between inter-individual genetic variation in PPAR signalling and diffusion properties of the white matter in preterm infants.
Conclusion This provides specific insight into how nutrition might be tailored with precision according to each infant’s genetic profile to optimize brain development.
Abstract no. 267 Usability across health information technology systems: searching for commonalities and consistency
Ross Koppel, University of Pennsylvania, Philadelphia, USA
Craig Kuziemsky, University of Ottawa, Ottawa, Canada
Introduction HIT usability issues remain a prominent source of medical errors and other unintended consequences. While research has identified usability issues in a single system or setting, the challenge of usability across a range of systems remains problematic. Patient care increasingly occurs across multiple providers, settings and HIT systems, and thus usability must be considered not just for one system, but across several systems and users. Functions or features in HIT (e.g. data retrieval or display) may not be designed consistently across systems and this can lead to errors and other unintended consequences.
Methods To examine the variables and interactions of how specific usability issues vary across different clinical systems we constructed a matrix of 11 usability dimensions and contextual differences. We built the matrix from the literature and from our collective 52 years of surveys, observations, shadowing clinicians, usability studies, etc. For this poster, we select four key usability dimensions and discuss how they contribute to the silent error of information retrieval. We also shall illustrate each of these with screen shots and analyses.
Results Finding patients and data reflects inconsistent navigation and search functions. Such problems are at least inefficient, and at worst, lethal. Inconsistent data displays, e.g., fonts, colours, metrics and interfaces etc. vary dramatically across systems. Providers become comfortable viewing data in a specific context and may be confused when the display changes. Last, the number of screens and patient charts open at once—presents patient safety dangers and trade-off. Each additional chart or screen increases the probability of entering an order into the wrong patient chart or reading data from the wrong patient chart.
Discussion We sought to examine multi-setting, multi-systems, and multi-user matrix of usability dimensions and contexts. The proposed research will hopefully encourage a more panoptic design of HIT software by incorporating the need to focus on usability across several facilities and many software vendors’ products.
Conclusion HIT systems and functions will always be emergent, interactive and multifaceted. To make the systems useable across many settings and many users, vendors will have to incorporate equally emergent, multi-context and multifaceted approach to usability.
Abstract no. 273 Mobile applications to support depression self-management: a review of apps
Sharareh R. Niakan Kalhori and Hajar Hasannejadasl Farhad Fatehi, Tehran University of Medical Sciences, Tehran
According to the World Health Organization 33% of the years lived with disability (YLD) are attributed to neuropsychiatric disorders. WHO estimated that globally 350 million people suffer from depression. The effect of this burden on society is overwhelming. Meanwhile, self-management is an important aspect of required care in long-term disorders and diseases management. mHealth based tools such as smartphone applications have been recommended as new tools to support self-management in depression. In this review we assess on mobile application apps were focused on depression in English. Evaluation conducted based on 7 functionalities (such as inform, instruct). Of 251 potentially relevant apps, 68 met our inclusion criteria. However for self-management assessment 7 applications had the minimum eligibility. Given the complex challenges faced by patients with depression, there is a need for further app development targeting their needs. In addition development of multifunctional apps is required to support the management of depression along with other related mental disorders such as anxiety and stress concurrently.
Abstract no. 281 Predictive factors associated with intensity of physical activity of 12 month infants in environment of healthy living cohort study
Haider Raza, Gareth Stratton, and Ronan Lynos, Swansea University, Swansea
Shang-Ming Zhou, Swansea University Medical School, Swansea
Sinead Brophy, FARR Institute (CIPHER - Swansea), Swansea University, Swansea
Introduction It is well-recognized that physical-activity (PA) plays an important role in enhancing and maintaining health-related behaviors in children1. This study aims to examine factors associated with high-level PA at age 12 months compared to those who are relatively inactive.
Methods The Environments for Healthy-Living, Growing up in Wales cohort study collected questionnaire data and postnatal notes and linked this data with general practice and hospital admission records. In addition, a total 148 out of 800 infants wore a tri-axial GENEActiv accelerometer on their ankle to collect the physical activity data2 over 7 consecutive days. Activity was measured in the sum of vector (SVM) magnitudes and the population was divided into two using the median from a lower activity and a higher activity group. Important predictive factors were identified for a linear regression model to predict the PA levels.
Results The mean SVM score in lower active group ranged from [0.677, 4.932] SVM magnitude and in active group from [4.975, 10.628] SVM magnitude. Infants in the active group were more likely to be boys (i.e. 70.42% boys and 29.58% girls), whereas in the inactive group (i.e. 38.57% boys and 61.43% girls). Active infants have a longer gestation, more milk feeds per week, more likely to be breastfed for longer, more active at night, and drink more juice. There were significant differences between lower and higher active infants groups on the following factors defined by mean difference (MD) and confidence interval (CI)): mother gestation days (MD: -9.7 days, CI: [-16.235, -3.167]), where there is 12.8% of preterm birth (i.e. 260 days) in lower activity level and no such case exists in higher activity group, number of milk feed per week (MD: -2.923, CI: [-5.574,-0.272]), last breastfeed in weeks (MD:-7.877, CI:[-14.350, -1.404]), mean SVM of baby during night (i.e. 7:00 PM to 7:00 AM) (MD: -1.750, CI: [-2.5356, -1.234]), number of night walks per night (MD: 0.58, CI: [0.061, 1.091]), number of juice taken per week (MD: 1.076, CI: [0.025, 2.216]). Moreover, in the higher active group, 84.85% of mothers tried to breastfed their babies, whereas in the lower active group this percentage was reduced to 77.61%.
Conclusion There is a great deal of variability in the level of activity in different children. The active children are more likely to be those who are full-term, breastfed, active at night, and take juice. There was no significant effect on the size of the baby on the activity level however, the preterm birth is associated with lower activity level. The important factors identified by this study would benefit health decision making in promoting healthier lifestyles for infants and their mothers.
References
Pawlowski et-al, “Children’s physical activity behavior during school recess: a pilot study using GPS, accelerometer, participant observation, and go-along interview,” PLoS One, vol. 11, no. 2, pp. 1–17, 2016.
Morgan et-al , “Physical activity and excess weight in pregnancy have independent and unique effects on delivery and perinatal outcomes,” PLoS One, vol. 9, no. 4, pp 1–8, 2014.
Abstract no. 284 Dementia severity and progression: identifying those most at risk for rapid cognitive decline
Elizabeth Baker, Ehtesham Iqbal, Caroline Johnston, and Stephen Newhouse, Department of Biostatistics and Health Informatics, King’s College London, London
Matthew Broadbent, NIHR Biomedical Research Centre for Mental Health, King’s College London, London
Hitesh Shetty, South London and Maudsely NHS Foundation Trust, London
Robert Stewart, Department of Psychological Medicine, King’s College London, London
Robert Howard, Division of Psychiatry, University College London, London
Mizanur Khondoker, Department of Population Health and Primary Care, University of East Anglia, Norwich
Steven Kiddle, King’s College London and MRC Biostatistics Unit, London
Richard Dobson, Department of Biostatistics and Health Informatics, King’s College London, London
Introduction Stratifying patients based on predicted future rate of decline would help in the design of clinical trials. Trajectory modeling to detect patterns of decline is a challenge when little information on disease stage is available. The relationship between the rate of progression and disease severity can be used to identify dementia patients deviating from the expected pattern of change.1 Applying this approach with a cognitive phenotype has not yet been explored but could be used to identify those most at risk for faster cognitive decline.
Methods Due to the challenge of identifying a cohort with sufficient length of follow-up to effectively study disease progression, this study turned to the secondary use of electronic health records. Specifically, a retrospective cohort was derived from South London and Maudsley NHS Foundation Trust health records comprising 3441 subjects with at least 3 MMSE scores recorded over 5 years. Residuals from the relationship between cognitive decline and disease severity were grouped into tertiles of average, slower and faster progression. Subject characteristics were explored for association with group membership by multinomial regression. Characteristics including demographics, items from the Health of Nation Outcome Scales (HoNOS) and promising repurposing medications for dementia2 were available for comparison across groups.
Results A quadratic relationship between the rate of cognitive decline and disease severity was observed in this health record-derived cohort. In the multinomial regression analysis, HoNOS items indicating presence of Hallucinations (and/or delusions) [Relative risk ratio (RRR) = 1.5, 95% Confidence Interval (CI) 1.1-2.05] and presence of cognitive problems [RRR = 1.57, 95% CI = 1.24-1.98] were associated with an increased risk of being in the faster progression group. Prescription of Olanzapine [RRR = 2.49, 95% CI = 1.43-4.35] and HoNOS item for the presence of problems with work or leisure activities [RRR = 1.31, 95% CI = 1.01-1.7] were associated with a higher risk of being in the slower progression group. Prescription of Galantamine was associated with being in the slower progression group compared to the average group [RRR = 0.481, 95% CI 0.292-0.792].
Discussion A presence of psychotic symptoms and prescription of antipsychotic medications showed increased risk of being in both the fast and slow progression groups. This may reflect differences in reporting of psychotic symptoms and possible early monitoring of dementia in patients with other mental health difficulties. Further exploration and replication in the Camden and Islington NHS Foundation trust health records are underway.
Conclusions This study demonstrates how health records can be used to suggest potential relationships between patient characteristics and future disease progression.
References
Villemagne, V. L, et al, Australian Imaging, Biomarkers and Lifestyle Study of Aging group. Amyloid beta deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer’s disease: a prospective cohort study. Lancet Neurol 2013 12(4), 357-367.
Appleby BS, et al, A Review: Treatment of Alzheimer’s Disease Discovered in Repurposed Agents. Dementia and geriatric cognitive disorders. 201335:1-22.
Abstract no. 289 Steps to modeling of physical-activity-related adherence in patients with heart disease. Literature review on adherence influence factors
Kristina Livitckaia, Nicos Maglaveras, and Ioanna Chouvarda, Aristotle University of Thessaloniki, Laboratory of Computing and Medical Informatics, Thessaloniki
Introduction The problem of patient adherence is becoming alarming in the medical practice worldwide. Formation of patient adherence (PA) depends on many factors. Specifically, there is limited data on PA to particular lifestyle recommendations. Considering physical activity and exercise as an essential part of lifestyle to control cardiovascular disease (CVD) and prevent its further progression, the review was focused on discovering the factors associated with physical-activity-related adherence in a group of patients with CVD that can be used under eHealth interventions development. The objectives of the review included: (a) identification of types of physical-activity-related behaviour and its settings, (b) assembling adherence measurement criteria, (c) identification and classification of factors affecting adherence.
Methods A comprehensive literature review was conducted based on the scoping studies approach1. Where applicable, the systematic review methods were used to narrow and increase the quality of the final results. The MEDLINE database and the Cochrane Library were accessed between March and August 2016. Out of original 277 yielded publications, 58 were included for further analysis. Considering the manual search queries, 5 relevant papers were added to the final results.
Results PA is an indicator of the performed level of physical activity in everyday life, as well as during cardiac rehabilitation. Attention was paid to the perspectives from which the term was considered in the selected studies. In regard to adherence, the types and settings of physical-activity-related behaviour were determined and classified. Finally, the measure instruments used up to date for adherence were overviewed and briefly described. Patient adherence to physical-activity-related behaviour reflects a complex interaction of different factors. Examined factors have been classified with regard to the nature of its origin and association to PA. Statistically significant factors and their influence on PA to physical-activity-related behaviour are discussed in the review.
Discussion Intervention settings: Majority of the interventions was heterogeneous and not comparable with regard to participants’ characteristics, types and settings of physical-activity-related behavior, and intervention settings. Patient adherence: The selected studies provide different types and dimensions of adherence, depending on the particular behaviour, measurement methods and instruments. Associated actors: The biggest challenge in understanding the influence of certain factors is that adherence is multifactorial.
Conclusion Intervention classification: There is a need for intervention classification, the results of which could be used in the design of eHealth interventions. Modifiable factors analysis: Actuality for dividing factors to modifiable and non-modifiable, from the perspective of eHealth interventions development, requires further investigation. Prediction algorithm: Attention has to be given to the eHealth interventions that already exist in the clinical practice. Together with the recent results, it may serve a solid background for the development of the prediction algorithm for early identification and prognosis of patients’ (non-) adherence.
Reference
H. Arksey and L. O’Malley (2005) “Scoping studies: towards a methodological framework”, Int. J. Soc. Res. Methodol., 8(1):19–32.
Abstract no. 294 Intensive care decision making: acute physiology and chronic health evaluation II vs. simplified acute physiology score II: a prospective cross-sectional evaluation study.
Alireza Atashi and Mirmohammad Miri, Mashhad University of Medical Sciences, Mashhad
Zahra Rahmatinezhad, Breast Cancer Research Center, ACECR, Tehran
Saeid Eslami, Student Research Committee, Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad
Masoumeh Sarbaz, Shahid Beheshti UMS, Tehran
Introduction Accurate outcome prediction by the means of available clinical contributing factors will support researchers and administrators in realistic planning, workload determination, resource optimization, and evidence-based quality control process. This study is aimed to evaluate APACHE II and SAPS II prediction models in an Iranian population.
Methods To calculate APACHE II and SAPS II for all consecutive patients admitted to intensive care units, a prospective cross-sectional study was conducted in four tertiary care referral centers located in the top two most populated cities in Iran, from August 2013 to August 2015. The Brier score, Area Under the Receiver Operating Characteristics Curve (AUC), and Hosmer-Lemeshow (H-L) goodness-of-fit test were employed to quantify models’ performance.
Results A total of 1799 patients (58.5% males and 41.5% females) were included for further score calculation. The Brier score for APACHE II and SAPS II were 0.17 and 0.196, respectively. Both scoring systems were associated with acceptable AUCs (APACHE II = 0.745 and SAPS II = 0.751). However, none of prediction models were fitted to dataset (H-L ρ value < 0.01).
Conclusion With regards to poor performance measures of APACHE II and SAPS II in this study, finding recalibrated version of current prediction models is considered as an obligatory research question before applying it as a clinical prioritization or quality control instrument.
Abstract no. 308 Subgroup biomarker identification: an information theoretic insight
Emily Turner and Konstantinos Sechidis, University of Manchester, Manchester
Paul D. Metcalfe, Advanced Analytics Centre, Global Medicines Development, AstraZeneca, Cambridge
James Weatherall, Advanced Analytics Centre, Global Medicines Development, AstraZeneca, Alderley Park
Gavin Brown, School of Computer Science, University of Manchester, Manchester
Our work provides a theoretical and experimental comparison of three prominent methods for exploratory subgroup identification. We provide an information theoretic interpretation of the problem and connect it with the three methods. We believe that this interpretation brings additional clarity to the comparison. Our conclusions are that Virtual Twins (Foster et al. 2011) performs best by several measures. However, it appears to have weaknesses in distinguishing between predictive and prognostic biomarkers.
Abstract no. 311 Use of cognitive and behavioral theory in clinical decision support systems: systematic review
Stephanie Medlock and Ameen Abu-Hanna, Academic Medical Center, Amsterdam
Introduction Decision support is widely regarded as one of the most promising means for information technology to improve care. However, to date, its ability to live up to that promise has been inconsistent. The recently-published “Two Stream Model” proposes that decision support can be described in terms of the clinical stream (reasoning about what advice to present) and the cognitive stream (reasoning about how to present that advice to the user). It suggests that cognitive/behavioural knowledge should be used to determine what support the user needs and how the system should provide it. The objective of this review is to evaluate whether and how knowledge from three diverse areas of cognitive science - descriptive decision theory, human-computer interaction, and behaviour change theory - have been applied or proposed for application to the field of computerized clinical decision support.
Methods A search was conducted for each of the three areas of cognitive/behavioural science by a Master’s student in Medical Informatics. The searches used Medline (all searches), Google Scholar (for human-computer interaction) and Embase (for behaviour change theories) and followed the general form: ((area of cognitive-behavioural science) and (decision support)). The searches were conducted in January 2016. Articles were included if they described a computerized decision support system, or a proposal for designing such systems, and described the use of a descriptive decision theory, human-computer interaction, or behaviour change theory in the design or evaluation of that system. Papers which used one of these approaches exclusively to analyze the results of a usability evaluation were excluded. Data were extracted on the study year, the cognitive/ behavioural theory used, how it was used, and the decision support system where it was applied. Data extraction was checked by a second reviewer.
Results A total of 15 studies were included: 5 incorporating descriptive decision theory, 5 using human-computer interaction, and 5 using behaviour-change theory. The studies using descriptive decision theory mainly used methods from this field (cognitive task analysis and theory of situation awareness) to collect observations used in system design. One study used Norman’s Theory of Action to categorize system use problems in evaluation of an existing system. Knowledge from human-computer interaction was used in both design and evaluation. Two proposed general principles for the design of systems, two described observation methods during evaluation, and one proposed a tool for evaluating human factors principles in medication alerts. Behaviour change theory was used exclusively in the design of patient-oriented systems, mainly for smoking cessation (4 studies). Four employed the trans-theoretical model one used the PRIME theory.
Conclusions Although these results should be considered preliminary due to the limitation that each search was carried out by a single researcher, these results suggest that knowledge from the field of cognitive and behavioural science has seen only limited use in the field of clinical decision support systems. These three areas of cognitive science were chosen due to their clear relevance to the field of decision support. However, further research should extend this to other areas of cognitive/ behavioural science.
Abstract no. 318 The RADAR Platform: an open source generalized mhealth data pipeline
Francesco Nobilia, Maximilian Kerz, and Richard Dobson, Department of Biostatistics and Health Informatics, Institute of Psychiatry Psychology and Neuroscience, King’s College London, London
Joris Borgdorff, Maxim Moinat, and Nivethika Mahasivam, The Hyve, Utrecht
Herculano Campos, Goldenarm, New York
Mark Begale, Vibrent Health, Fairfax
Amos Folarin, Farr Institute of Health Informatics Research, UCL Institute of Health Informatics, University College London, London
Introduction We are presently witnessing a new epoch in the evolution of mobile technologies defined by the proliferation of network aware, compute capable devices dubbed the ‘IoT Age’. A distinct category of these are personal wearable devices or ‘wearables’ which have an enormous potential application in the fields of healthcare and medical research. Real time data streaming capabilities represent an innovative and promising opportunity for mobile health (mHealth) applications based on remote sensing and feedback. The €22m IMI2 Remote Assessment of Disease and Relapse – Central Nervous System (RADAR-CNS http://radar-cns.org) initiative is a new research programme aimed at developing novel methods and infrastructure for monitoring major depressive disorder, epilepsy, and multiple sclerosis using wearable devices and smart-phone technology. While a number of commercial mHealth solutions are available to aggregate sensors data, there is a lack of an open source software stack that provides end-to-end data collection functionality for research, clinical trials and real-world applications. The RADAR platform aims to fill exactly this gap, providing generalised, scalable, fault tolerant, high-throughput, low-latency and data collection solution.
Methods By leveraging open source data streaming technologies, we are building an end-to-end system with generalized aggregation capabilities. The platform will focus on classes of data rather than specific devices, in doing so it will enhance modularity and adaptability as new devices become available. The platform is delivered under open source licence in order to create a legacy to downstream RADAR projects and the wider mHealth community. The key components of our software stack include: Data Ingestion and Schematisation (using Apache AVRO), Database Storage and Data Interface, Data Analytics, Front-end Ecosystem, Privacy and Security.
Results We have utilised the Confluent Platform as a core component, this is a new open source suite of tools built on top of Apache Kafka. At fixed intervals of time, the patients’ data sources collect data representing the patient’s attributes by passive (e.g. from hardware sensor streams) or active (e.g. questionnaires, assessments apps) ways. These attributes are then ingested via an HTTPS interface which translates REST calls in native Kafka calls. After a restructuring phase, data (both real-time and historical) are simultaneously analysed and persisted. Two different data warehouse layers (cold and hot storage) are deployed to provide low latency and high performance data access via controlled interfaces.
Discussion So far, we have demonstrated integration of the Empatica E4 wearable device and on-board smartphone sensors as examples of passive data sources and a Cordova questionnaire app builder as an example of an active remote monitoring data sources. Low latency data access tools and REST APIs serve as downstream generalised data access interfaces, examples of which are modular data visualisation tools.
Conclusion RADAR-CNS aims to improve patients’ quality of life, through self-management for example, and potentially change how these and other chronic disorders are treated. Its vision is to reduce: i) cost ii) trauma to the patients and carers iii) hospitalisations, by predicting and pre-empting relapses and recurrences via the use of remote assessment technologies.
Abstract no. 323 Mapping clinical care and research data to HL7 FHIR to improve sharing and reuse
Hannes Ulrich, Ann-Kristin Kock-Schoppenhauer, Björn Andersen, Petra Duhm-Harbeck, and Josef Ingenerf, Universität zu Lübeck, Lübeck
Introduction The increased adoption of electronic health records (EHRs) could enable better care for patients by sharing the collected clinical data between care providers and besides better clinical research through secondary use of EHR data. Clinical research is highly dependent on this data, such as demographic information, clinical data, or device observations. Every medical centre in the western world maintains a hospital information system (HIS), usually consisting of many subsystems, that support the clinical workflow, patient safety, and legal demands. Especially in university hospitals, physicians often have obligations in addition to clinical routine: In cooperation with other scientists they conduct clinical or experimental research.
Method In order to ensure the reproducibility of research results and for preserving raw data, the general trend favours the pooling of research data in standardized formats. The emerging HL7 Fast Healthcare Interoperability Resources (FHIR) aims to increase interoperability, information integrity, as well as the implementability of data exchange tasks in the healthcare and research IT environment. With focus on the research platform system CentraXX (Kairos GmbH) for biobank and study management, HL7 FHIR is used in this work to improve the semantic integration, (re-)use of relevant data and for connecting point-ofcare medical data. The transition from the research platform to the central clinical repository is desired to be simple, robust, and direct. In order to align and to map the proprietary CentraXX data model to FHIR resources, the underlying relational database schemata were analysed to specify the scope and the requirements for the desired mapping. In the following step, the list of HL7 FHIR resources was reviewed, considering the usage, limitations, and relationships of each.
Results Regarding the fast development of HL7 FHIR, we decided to use a pre-released standard for trial use in version 3 (STU3) in anticipation of upcoming resources changes. Therefore, our mapping can easily be adjusted to the impending update. Eight suitable resources were identified: Patient, Encounter, Observation, Condition, Procedure, Diagnostic Report, Specimen and Consent. Some values are initially missing in the Patient resource, so a specific profile was created to represent the patients’ nationality and blood group.
Discussion Any mapping relies on the experts who design the mapping. The validation of the mapping was performed by one medical expert and one technical expert independently, which agreed despite of minor differences. So, a clinical repository allows for the reuse of data, yet the threat of misinterpretation of the data remains. Despite our efforts, there is no complete certainty that the acquisition context and purpose is fully represented in the repository. To minimize this threat, further tools and data quality monitoring efforts are required.
Conclusion The comprehensive clinical repository introduced in this paper combines patient demographic and clinical data including device observations in a standardized way. The data collected along the way of a patient’s progression through the healthcare system can thus now be reused for further research projects. As all information is represented in a standardized way, the exchange and pooling of data is significantly simplified.
Abstract no. 332 Addressing SNOMED CT description logic modelling errors in issues with finding site
Heike Dewenter and Sylvia Thun, Niederrhein University of Applied Sciences, Krefeld
Kai U. Heitmann, HL7 Deutschland e. V., Köln
Introduction The use of SNOMED CT in medical coding is constantly progressing on an international level.1 However, description logic modelling errors related to the hierarchical structure have been described before on aspects of anatomy and clinical findings. Examples for problem types are especially “issues with site and resulting interferences”.2 We present an additional approach to address SNOMED CT logic modelling errors that affect issues with finding site. Our entry point for improvement is primarily substantial and focuses on concept definitions provided by literature.
Methods For examining exemplary description logic modelling errors, we use the SNOMED CT International Version 20160731. We take the concept |414086009|Embolism (disorder)|, that is erroneously defined as a |19660004|Disorder of soft tissue (disorder)|. After the analysis of associated concept definitions for “embolism” and “soft tissue”, the hierarchical connections are adjusted.
Results In SNOMED CT, there is the distinction between |414086009|Embolism (disorder)| and 55584005|Embolus (morphologic abnormality)|. Embolism is defined as a condition where the blood flow in an artery is blocked by a foreign body. Soft tissue includes all kinds of tissues inside the body, except bone. To improve the modelling, we take an alternative definition of “embolism” connected to the specification of a certain finding site, especially in the bloodstream.3 According to this definition and with the intention to include the “site aspect”, it seems rather an option to create a new hierarchical connection that defines:
|414086009|Embolism (disorder)| is a |43195004|Bloodstream finding| is a |118234003|Finding by site|.
Discussion In clinical practice, embolism is considered as a finding of the blood stream rather than a soft tissue disorder. Soft tissue seems to be unsuitable as a classifier concept because of the broad term definition. The finding site is needed to indicate the location of embolism that is the cause of the incorrect classification. In order to rearrange the hierarchy, the finding site of blood vessel has to be changed to enable the expected description logic modelling result.
Conclusion It will be a considerable huge effort to filter all concepts that seem to have an “issue with site”, with regard to the definition of the term ‘soft tissue’. However, the assignment of new or alternative parent concepts will not solve the problem. It is not ideal to declare all embolism disorders as primitive concepts. It seems to be the best option to limit the content improvement on single use cases and to get more evidence on whether keeping soft tissue disorder as a classifier or to retire it.
References
Lee D et al. Literature review of SNOMED CT use. J Am Med Inform Assoc. 2014 Feb 21(e1): e11–e19
Rector AL et al. Getting the foot out of the pelvis: Modelling problems affecting use of SNOMED CT hierarchies in practical applications. J Am Med Inform Assoc. 2011 Jul-Aug 18 (4):432-40
Dorland’s. Dorland’s Illustrated Medical Dictionary (32nd edition). 2012. Elsevier. p. 606.
Abstract no. 341 Big difference? Harnessing big data for two of the world’s largest biobanks
Sam Sansome and Ligia Adamska, University of Oxford, Oxford
Introduction The University of Oxford is engaged with some of the world’s largest population-based, prospective studies. The UK Biobank (UKB) and China Kadoorie Biobank (CKB), each of which recruited over half a million participants, will be based at the University of Oxford’s Big Data Institute (BDI). The BDI will be directed at obtaining and characterising large data-sets to significantly alter our understanding of the causes and treatment of disease. Both studies will have a unique opportunity to share what they have learnt from each other and to identify new opportunities in the future.
Methods UKB and CKB both recruited half a million middle aged participants between 2004 and 2010. Participants were selected from different regions across the UK and China, and have undergone extensive baseline measures, provided blood and urine for future analysis and gave detailed information about themselves. Participants’ health outcomes will be closely followed over the next few decades through linkage with established death and disease registries, and national health databases. Both studies run periodic repeat assessments on a subset of their cohorts and implement project enhancements such as genotyping, physical activity assessment, biochemistry, proteomics and new web-based questionnaires to collect even more data on their participants.
Results Collectively, both biobanks have combined health-related data on over a million people, stored over 3.5 million blood samples and have linked participants to 45,000 death records, 100,000 cancer records and 1.5 million hospital admission and health records. UKB is an open access resource with over 3,619 registered researchers, 343 projects underway and 1,256 released datasets. CKB has its own researchers in the UK and China, and it is only just starting to make its data available to the public. CKB now has over 300 registered researchers.
Discussion Whilst the two studies began with separate operating and funding models, we have identified many opportunities to share methodologies such as genetic assays, record linkage, encryption, anonymisation and delivery of research datasets.
We are looking for further opportunities to explore new and novel big data opportunities by working closely together. We have begun sharing our knowledge and experience with integration of new data sources such as physical activity monitors, electronic data capture devices, imaging data, outcome adjudication and linkage of our participants to air pollution and other meteorological data.
We are also collaborating and attracting the attention of other departments within the university to develop machine learning techniques on data such as the ECG to predict future outcomes for our participants and validate these through our continual follow-up.
Conclusion These two studies are powerful resources for investigating the main causes of many common chronic diseases over the next few decades, and the information generated will advance our understanding of disease aetiology in the UK, China and in other countries. This collaboration also presents an invaluable opportunity for sharing of methodologies related to integration, processing, dissemination and storage of health-related data. This has led to high quality research and contributed immensely to understanding of disease and contributing factors.
Abstract no. 353 Patient flow modelling and scheduling using point interval temporal logic
Irfan Chishti, Artie Basukoski, and Thierry Chaussalet, University of Westminster, London
It has been increasingly recognized that a consistent process model and its implementation, such as a simulation, can be instrumental in addressing the multi-faceted challenges health care is facing at present and more importantly in the future. However, current modelling and scheduling techniques are intuitive normally depicted as flowcharts or activity diagrams. These diagrams provide vague descriptions that cannot fully capture the complexities of the types of activities, and types of temporal constraints between them, i.e. finish-start barrier, which are essential for the application of the critical path method(CPM) and project evaluation review technique (PERT). This paper proposes a framework to model patient flows for precise representation based on point interval logic (PIL). The framework consists of steps that can be applied to transform activity diagrams to a Point Graph (PG) which has a formal translation to PIL. We will briefly evaluate an illustrative discharge patient flow example initially modelled using Unified Modelling Language Activity Diagram (UML AD) with the intention to compare with the technique presented here for its potential use to model patient flows.
Abstract no. 357 Impact of linkage error in a national mother-baby data linkage cohort
Katie Harron and Jan van der Meulen, London School of Hygiene and Tropical Medicine, London
Ruth Gilbert, University College London, London
Introduction Linkage of administrative data is an important tool for population-based epidemiological analyses. Evidence suggests that errors in linkage disproportionally affect particular subgroups of individuals, yet records that fail to link (missedmatches) are typically discarded and not used in analysis. This means linked dataset may not be representative of the study population and may produce biased results for research or service evaluation. We explored the impact of linkage error in a previously linked, population-based cohort of mothers and babies.
Methods We used a gold-standard subset of data from 15 NHS maternity units for 2012 to 2013, to validate previous mother-baby linkage within national hospital admission data for England (Hospital Episode Statistics HES). We compared characteristics of the gold-standard data and linked data. We also explored the effect of a more conservative linkage approach, aiming to limit the false-match rate (where baby records link to the wrong maternal record).
Results Of 72,824 records in the gold-standard dataset, 632 (0.9%) were false-matches and 297 (0.4%) were missed-matches in the original linked mother-baby HES data. Using the conservative probabilistic linkage algorithm resulted in fewer false matches (212 0.3%) but substantially increased the number of missed-matches (7,797 10.7%). Records that failed to were more likely to have missing data, and be of lower birthweight and gestational age, or still births.
Discussion Quality of linkage in the national mother-baby dataset is high, but specific subgroups of interest are less likely to link due to poorer data quality. Reducing the false-match rate disproportionately affects the number of unlinked records and restricts the number of records available for analysis.
Conclusion Since linkage error and missing data are intertwined, future research will explore the potential for statistical methods such as multiple imputation to account for bias due to both missing data and missed-matches. Additional methods are required for identifying and handling bias arising from false-matches.
Abstract no. 364 Design of the rural health centres locating model using Geographic Information Systems
Marzieh Saremian, Lorestan University of Medical Sciences, Khorramabad
Reza Safdari and Marjan Ghazisaeedi, Tehran University of Medical Sciences, Khorramabad
Abbas Sheikhtaheri, Iran University of Medical Sciences, Khorramabad
Introduction Lack of health services, especially in rural areas, small towns and poor areas, will bring a lot of negative consequences. In recent years, the use of information and communication technologies in healthcare, improve the quality of health care. Because the health of society depends on the health service centres, and health centres directly involved in providing the health of individuals and society, so easy and timely access to these centres is essential. The decision about the construction of health centres based on the number, density and health problems of people and type of provided services, are issues that Geographic Information Systems (GIS) can help to solve them through spatial analysis. A part of the GIS software package is Model Builder. This is the tools to create, modify and manage the models. Therefore, the use of GIS in the establishment and distribution of health services, particularly in rural health centres, increases efficiency in the field of healthcare.
Method In this applied research, the rural health centres locating model was designed using GIS software. At first, the study area’s maps, using GIS and application of Georeferencing and Digitizing functions, were prepared. Further, for effective locating criteria and sub-criteria for rural health centres, separated layers were defined. For such actions, we used Arc Map, Arc Catalog and Arc toolbox environments of Arc GIS (version 9.3). Then using the maps and layers, we designed the model by defining inputs, outputs and processes in the model builder environment of GIS.
Result In order to design the locating model, initially in Arc Catalog, a Toolbox was built. Then in the Arc Map and by Model Builder, the model was designed. The designed model allow that by entering maps and information layers, and run the model, the needed analysis for optimal locating carried out. Finally, a map was prepared that indicated the possibility of appropriateness to establish the rural health centres in the study area.
Conclusion The use of technology such as GIS in locating of rural health centres, can improve the quality of planners’ work, as well as enhance the quality of people life, particularly people those living in rural and remote areas that are deprived of many resources, especially in the health field. Future plan: The use of GIS in locating of health centres and hospital is recommended. Also it is recommended that Proper distribution hospitals and other health centres be reviewed so that if necessary, appropriate measures carried out.
Abstract no. 365 Application and reuse of metadata for healthcare quality indicators
Pam White, Kent County Council, Maidstone
Abdul Roudsari, University of Victoria, Victoria
Introduction There have been numerous simultaneous quality initiatives in the United Kingdom’s National Health Service (NHS), including Payment By Results, the Quality, Innovation, Productivity and Prevention programme and the NHS Quality and Outcomes Framework1. When more than one governing body issues similar quality indicators, they are prone to inconsistencies in formatting and may overlap in content.
Method We developed a pilot ontology that specifies inclusion and exclusion criteria, along with relationships between quality indicators and categorises of indicators. Our ontology is intended to make components of the indicators searchable. We recorded metadata, sourced from NHS Digital, for a set of 222 quality indicators2 in the ontology. We explain further detail about conceptualisation of the ontology in a separate article.3
Results Inconsistent/incomplete metadata for some indicators contributed to inconsistent definitions for properties, including Source and Formula. Some indicators were part of a named set. Some were single indicators. Indicator set names were not standardised. The source for one set was sometimes listed as ‘UK Renal Registry’. Other times, it was listed as ‘National Renal Dataset’. The formula metadata supplied by NHS Digital was inconsistent, sometimes with just a referring URL and other times with extensive detail that included non-formulaic information. The referring URL was occassionally a broken link and no formula could be found. Other links led to a general website, with no clear link to methodology. The formula was sometimes more clearly presented in the other sections of the NHS Digital metadata than in their Formula section.
Discussion NHS Digital’s Metadata Library Guide for the indicators was largely based on the United Kindgom’s e-Government Metadata Standard.4 In practice, the guide includes vague explanations for some metadata elements. The International Federation of Library Association’s5 Statement of International Cataloguing Principles could be used as a starting point to develop standards for metadata for quality indicators. A companion guide to these principles, Resource Description and Access, has been made available by the Joint Steering Committee for Development of RDA.6
Variations in origin and complexity of the indicators may count for some inconsistencies we found in the metadata from NHS Digital. It is difficult to standardise metadata for formulae for indicators developed outside a descriptive framework, due to differing calculation methods. The US National Quality Forum7 has developed a framework for health indicator metadata, based on data available in electronic health records. Alternatively, a diverse set of quality indicators may be represented with components of indicators described separately with names or codes of separate metadata elements summarised in the Formula metadata field.
Conclusion Consistent and accurate metadata to support access to quality indicators is crucial to establishing indicator interoperability. The recommendations in this paper pertain to metadata for identifying healthcare quality indicator source and formula. Further exploratory work to analyse descriptive information about other indicator sets could inform the development of international guidance for quality indicator metadata.
References
http://www.nhsemployers.org/
https://mqi.ic.nhs.uk/IndicatorsList.aspx
Stud Health Technol Inform. 2015208:347-51.
http://www.esd.org.uk/standards/egms/
http://www.ifla.org/files/assets/cataloguing/icp/icp_2009-en.pdf
http://www.rda-jsc.org/docs/5rda-objectivesrev3.pdf.
http://www.qualityforum.org/WorkArea/linkit.aspx?LinkIdentifier=id&ItemID=22019
Abstract no. 370 Quanti-Kin Web: a web tool for ELISA assay processing
Mauro Giacomini, Simona Bertolini, Isabella Martini, Jennifer McDermott, Elena Lazarova, Giorgia Gazzarata, Roberta Gazzarata, and Oliviero Varnier, University of Genova, Genova
Diagnosis and treatment monitoring can be greatly improved if numerical values of laboratory markers are available. In several laboratories various tests, such as the enzyme-linked immunosorbent assay (ELISA), are based on colorimetric methods, where enzyme activity is used as a quantitative label. The ELISA is an easily standardized and readily automated, relatively inexpensive, highly sensitive and specific procedure, which requires small sample and reagent volumes. The accuracy and wide range of quantification with the ELISA method is still an open problem. This paper presents the improved and web based version of a software for analyte quantification that bases its quantification capability on optical density readings collected both during the colour formation phase and after the dispensation of the stop solution.
The Quanti-Kin Web has been developed within Microsoft Visual Studio 2015 using Microsoft SQL Server 2016 to record data and is now available at the address http://www.quantikin.com. The complete process of test management can be divided into five sections.
1. Creation of a new assay.
2. Optical density values acquisition.
3. Web based data transfer.
4. Quality control of the experiment.
5. Quantification of analyte and results reporting.
A large quantity of old data related to the calibration curves of two important experimental centres and data produced in the same centres, but by training personnel visiting these centres, was collected. Therefore, with these types of data it was possible to evaluate the efficiency of the calculation engine. The previous program QKDS achieved good precision regarding the quantification of known amounts of p24, but the data presented were only produced in an extremely well controlled environment. The statistical analysis performed with the data collected by highly trained users shows that Quanti-Kin Web produced results similar to those presented in. On the contrary, during widespread worldwide routine use the performances in quantification were significantly lower than those obtained by well trained personnel and reported in. This aspect can greatly influence quantification results and the curves. Thanks to Quanti-Kin Web improved quantification algorithm, this problem can be overcome without affecting the quality of the experiment. In fact a strong check on the wells has been developed. By this way, the maximum error has been significantly reduced from 960.49 in the old version to 55.63 in the new one. The standard deviation was also reduced from 86.53% to 6.7%, the variance was reduced from 74.87 to 44.87 and lastly the mean error was reduced from 15.19 to 0.24. The data are calculated over experiment performed in many laboratories all over the word during the last three years.
The web deployment of the present tool makes its use very simple, as it does not require any installation and it assures a very fast execution. The data that are exchanged on the web are uniquely related to the amount of analyte present in the wells and not to the identity of the patient, so any restrictions due to the privacy laws of many countries are not affected.
Abstract no. 377 A CTS2 compliant solution for semantics management in laboratory reports at regional level
Roberta Gazzarata and Mauro Giacomini, University of Genova, Genova
Maria Eugenia Monteverde, Healthropy s.r.l., Savona
Elena Vio and Claudio Saccavini, Arsenàl.IT, Treviso
Lorenzo Gubian, Veneto Region, Venezia
Idelfo Borgo, Local Health Authority 16 of Padova, Padova
The clinical data sharing represents a fundamental tool to improve the clinical research, patient care and reduce health costs. The Health Ministries of many developed countries are planning the creation of national health information exchange (HIE) systems by defining the functionalities to support the sharing of the knowledge of their content. To realize distributed system architectures able to satisfy this requirement, the management of semantics is a critical and essential aspect that must be considered. For this reason, a research is now underway to set up an infrastructure able to aggregate information coming from health information systems, and it will be experimented to support regional HIE in Veneto Region. In this paper the first steps of this research and the current implementation state are presented.
The first period focused on the semantics management in laboratory reports. As indicated by the Italian Health Ministry, laboratory reports must be structured adopting the HL7 Clinical Document Architecture Release 2 (CDA R2) standard and LOINC vocabulary. For this reason, LOINC was used as reference code system. To manage the semantics of the information involved in the contextual workflow, the design and the implementation of a terminology service was considered and the Common Terminology Service Release 2 (CTS2) standard, product of Healthcare Service Specification Project3, was adopted. In this phase, the authors selected 6 CTS2 terminology resources (codeSystems, codeSystemVersions, EntityDescripctions, Map, MapVersion and MapEntry) and, for all these, decided to start from the implementation of read, query, maintenance and temporal functionalities. The SOAP (Simple Object Access Protocol) was chosen as implementation profile and Microsoft Windows Azure was adopted as cloud platform to host both database and web services.
The proposed solution is formed by the regional HIE, 22 Laboratory Information Systems (LISs) of the local departments of the Veneto region, the terminology service, called Health Terminology Service (HTS), and an application to manage the content of the terminology database. The core of the architecture is the HTS that provides access to the terminology database through interfaces compliant to the CTS2 standard. At the present, the HTS is formed by a Microsoft SQL Azure database (the terminology database), and eighteen Windows Communication Foundation (WCF) services, which represent the CTS2 interface, hosted on Microsoft Azure. The first client application that was connected to the HTS was the web application used to maintain the content of the HTS terminology database. It is continuously evolving to satisfy both the needs of medical staff and the requirements that the Veneto region is designing to create the regional HIE and to manage the semantics of its content.
This paper presents the current implementation state of the infrastructure proposed to manage semantics in laboratory reports at regional level. In the next months, the technical specification will be defined for the integration of HTS with 4 out of 22 LISs and with the regional HIE. After a validation period in which the solution will be tested, an analysis will be performed to evaluate its impacts.
Abstract no. 385 A ‘one health’ antibacterial prescription surveillance approach developed through the use of health informatics
Fernando Sanchez-Vizcaino, Daniel Hungerford, and Rob Christley, University of Liverpool, Institute of Infection and Global Health, Liverpool
Neil French, David Singleton and Alan Radford, NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, University of Liverpool, Liverpool
Introduction Evidence of antimicrobial resistance transmission between humans, livestock and companion animals highlights that a truly ‘one health’ approach is needed to preserve antimicrobial efficacy. Antibacterial use is considered as the key driver for the development of antibiotic resistance bacteria. It is therefore essential to understand how widely antibacterials are being used across both human and animal health. However, tools for integrating data sources contributed to by both human and veterinary healthcare have not been developed yet, nor has the extent to which small companion animals contribute towards zoonotic antimicrobial resistant transmission been investigated. The objective of this study is to demonstrate the feasibility of a novel ‘One Health Informatics’ approach for comparing antibacterial prescribing practices in human and small animals healthcare settings through the use of electronic health records (EHRs) obtained from a UK sentinel network of medical and veterinary practices.
Methods Medical data were collected through NHS Liverpool Clinical Commissioning Group facilitation, from 26 general medical practices in Liverpool between June 2014 and May 2016. EHRs included patient information such as sex, age, residence, antibacterials prescribed and the consultation coding for both respiratory disease and gastrointestinal disease consultations. Veterinary data were gathered electronically in real-time by SAVSNET, the Small Animal Veterinary Surveillance Network, from 458 veterinary premises throughout the UK between April 2014 and March 2016. Each record included the animal signalment (including species, breed, sex, age, etc.), owner’s postcode, syndrome information and treatments including antibacterials.
Results & Discussion EHRs were obtained from 4,121,340 (n= 157,274 patients) human consultations and 918,333 (n=413,870 dogs) canine and 352,730 (n=200,541 cats) feline veterinary consultations. In humans, total antibacterial prescribing was less common (4.51% of consultations, 95%CI: 4.49-4.53) than in dogs (18.8%, 18.2-19.4) or cats (17.5%, 16.9-18.1). Beta-lactams represented the most commonly prescribed antibacterial class in humans (53.8% of prescriptions, 53.6-54.0), dogs (43.8%, 42.4-45.1) and cats (71.1%, 68.9-73.3). The most commonly prescribed antibacterial was amoxicillin in humans (30.6% of overall prescriptions, 30.5-30.8), clavulanic acid potentiated amoxicillin in dogs (28.6%, 27.3-29.9), and cefovecin, a third-generation cephalosporin, in cats (35.2%, 31.9-38.6). To understand these differences in prescribing between human and small animals healthcare settings it will be important to assess the different types of patient seen in medical and veterinary practice.
Conclusion These preliminary results demonstrate the feasibility of ‘One Health’ antibacterial prescription surveillance in a UK sentinel network of medical and veterinary practices. In future work, we will develop tools to track geographical and temporal changes in the overall prescribing and at syndromic level in human and animal health and to explore factors associated with variation in prescribing patterns within medical and veterinary consultations.
Abstract no. 392 Sleepylab: an extendable mobile sleeplab based on wearable sensors
Andreas Burgdorf and Stephan M. Jonas, Department of Medical Informatics, Uniklinik RWTH Aachen, Aachen
Jó Agila Bitsch, COMSYS, RWTH Aachen University, Aachen
Introduction According to the Robert Koch-Institute, 25 percent of German adults suffer from sleep disorders. Consequences of sleep disorders are dangerous and costly. A common sleep examination is the in-clinic polysomnography, which records vital, activity and other parameters. These examinations are costly and usually limited to a few nights per patient. A possibility to perform cheap, long-time monitoring of patients are smart wearable devices. Many consumer-grade smartwatches or fitness trackers are equipped with sleep monitoring applications. However, these applications are not based on scientific publications and are limited to vendor applications. Thus, data access or aggregation across multiple devices, which would be required for a clinical assessment, is limited or impossible.
Method To close this information gap, we developed the modular Android application SleepyLab: a mobile sleep laboratory based on wearable devices. The modular architecture allows for different hardware, and processing or visualization algorithms in one solution. Therefore, SleepyLab consists of a Core Application and three plugin-types: 1) monitor, 2) processing and 3) presentation plugins. Recorded data is stored and communicated by the core application in an interoperable format using the Medical Subject Headings (MeSH) to classify devices and body regions. Monitor plugins measure data from smartphone-internal or connected sensors. For each device or characteristic, a separate plugin can be installed and integrates seamlessly into SleepyLab. Inspired by polysomnography, the following monitoring plugins have been realized: 1-3) movement (smartphone, smartwatch, TI SensorTag), 4-5) cardiac activity (chest belt, smartwatch), 6) sound (smartphone), 7) brightness (smartphone) and 8) EEG (Emotiv Epoc+). Processing plugins analyze recorded (or already processed) sleep data with arbitrary algorithms. Algorithms can potentially detect sleep stages, activity or snoring based on recorded signals. A first realized plugin aggregates movement data into activity. Finally, presentation plugins display recorded or analyzed data or are other data endpoints. One implemented plugin allows users to view graphs of raw data. Another plugin exports data into csv files for further processing.
Results SleepyLab was evaluated in two different phases. First, the suitability of the developed plugins and corresponding sensors was evaluated. Second, several full-night recording were performed in self-tests. The plugin evaluations showed that most plugins were suitable to perform their task. The smartwatch performed best in detecting movements, while the chest belt measured a more accurate heart rate. The sound plugin performed best with external microphones and the brightness plugin’s quality strongly depended on the used device. The EEG plugin could store the recorded data as EDF+ files but the sampling rate was too high to directly communicate data to the core application. The full night records were performed without the EEG device due to its form-factor. Nevertheless, the visualization of movements, sounds and heart rates allowed the manual detection of different sleep phases without further processing. Disadvantages were high energy consumption of the smartwatch and the heat development of the smartphone, if charger wirelessly.
Conclusion The mobile Application SleepyLab records, processes and visualizes sleep-related data from wearable devices to supports sleep research and clinical practice with inexpensive and unobtrusive long-term monitoring.
Abstract no. 404 Impact of large flash memory and thin- client infrastructure for the EHR and PACS Sharing system with XDS/XDS-I
Hiroshi Kondoh, Tottori University Hospital, Yonago
Masaki MOCHIDA, Tatsuro KAWAI, and Motohiro NISHIMUARA, SECOM Sanin Co.Ltd., Matsue
Takeshi YAMAGUCHI, GE Healthcare Japan Co. Ltd., Tokyo
Daisuke IDE, IBM Japan Co. Ltd., Tokyo
We developed and operated ERP and PACS sharing system with XDS/XDS-I on thin-client infrastructure. Large flash memory was introduced at the renewal of server, it was surprisingly shortened the start-up time and display time. EHR on cloud server should be spread in future.
Abstract no. 408 Attitude and current utilization of telehealth applications among diabetic patients in king saud university medical city, Riyadh Saudi Arabia
Nejoud AlKhashan and Ahmed Ismail Albarrak, King Saud University, Riyadh
Introduction Previous studies have strongly suggested good outcomes from using telehealth to support the management of chronic diseases and its complications. Outcomes include, improve patient’s quality of life, increase satisfaction, less visits to the emergency department, and better control over the disease with less overall costs. In Saudi Arabia, it was estimated that 24% of the population are diabetic. Objectives: To assess the diabetic patients’ utilization and attitude towards telehealth applications in King Saud University Medical City (KSUMC).
Methods A cross sectional study was conducted in KSUMC between March and April 2015. A self-administered questionnaire was adapted from technology acceptance model (TAM) and distributed to diabetic patients. Descriptive and correlation analysis was then performed.
Results A total of 237 participants responded to the questioner (128 (54%) males, 109 (46%) females), with mean age 47±15 (mean±SD). Although 30% prefer to see a healthcare provider (HCP) in person, however results show a positive attitude towards utilizing telehealth (p < .001). This can be can explain by the high number of smartphone users (86%).
Conclusion The study results revealed a gap in communication between diabetic patients and healthcare providers. Telehealth could play a major role in bridging this gap this is supported by the high number of smartphones’ applications users and the participants’ positive attitude towards the utilization of telehealth. It is recommended to work on promoting utilization of telehealth applications by patients with chronic diseases to have better control over their disease.
Abstract no. 414 When do people read their health record? – Analysis of usage data of a national eHealth service giving patients access to their electronic health record
Isabella Scandurra, Örebro University, Örebro
Maria Pettersson, Inera AB, Stockholm
Maria Hagglund, Karolinska Institutet, Stockholm
Introduction eHealth services for citizens provide support for patients and families, as well as for healthcare professionals. In Sweden different eHealth services have been developed since the late 1990s and they are now used by millions of users. One of the national eHealth services that provides opportunities for increased participation in care is the Patient Accessible Electronic Health Record (PAEHR). To date (February 2017) over one million citizens (of 10 million inhabitants) have accessed their own electronic health record (EHR). In this study, we describe current usage by analysing log-data from the service. Who are the users, and how and when do they use the service?
Method Data collection of routinely captured usage data was administered by Inera AB, owner of all Swedish national eHealth services. Data was analyzed through IBM SPSS in accordance with the declaration of Helsinki. Queries for this quantitative study were created based on previously published results regarding concerns often expressed by healthcare professionals (HCP) as well as routinely captured log-data. Descriptive usage statistics were analysed towards such HCP concerns, e.g. increased workload due to worried patients reading but not understanding the PAEHR content.
Results Current status of the Swedish PAEHR is presented, e.g. number of users, demographic data (age, gender) in relation to log-in statistics. Regarding log-ins, first-time users and unique hits show that attention by national media has an impact a news cast resulted in 31,000 logged in compared to a week day average of 20,000. To date more than 1 million citizens have chosen to log in and the numbers are increasing. A newly connected region (Örebro) has an average of 500 new users a day. This can be compared to the first region (Uppsala) which during the first year (2012-2013) had approx. 100 new users a day, although the strategy then was not to advertise the service. In total 10,000 to 13,000 new users log in every day nationally. More women than men log in and their mean age are 23-32 years. The older the users get the less they use the PAEHR, however some users are older than 93 years. During weekends the activity decreases, as opposed to HCP expectations. More often, users log in on week days, e.g. on Monday morning.
Discussion Usage statistics were related to concerns of HCP, which seem to have little resemblance to reality. One concern was that the service would not provide benefit for patients, here contradicted by the increasing number of both first-time and recurrent users. However, such indicators need to be further analysed. Paper records and PAEHR usage are difficult to compare, due to lack of statistics regarding printout reading. Usage comparisons between PAEHR solutions of different counties would however be interesting.
Conclusion Recurrent concerns of mainly HCP seem to be contradicted by actual usage by patients. This may lead to a decreased controversy of how PAEHR is experienced by patients and HCP. Knowledge about how users actually use PAEHR may also improve the service as such.
Abstract no. 419 Longitudinal resource usage of transcatheter aortic valve implantation (TAVI) using Hospital episode statistics in England
Samuel Urwin, Kim Keltie, Azfar Zaman, and Richard Edwards, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle-upon-Tyne
Julie Burn and Andrew Sims, Newcastle University, Newcastle-upon-Tyne
Introduction Transcatheter aortic valve implantation (TAVI) is a minimally invasive method of replacing the aortic valve in patients with aortic stenosis who are considered inoperable or high risk for surgical aortic valve replacement. The aim of this study was to estimate in-patient NHS resource usage following TAVI procedures using routine hospital administrative data from the Hospital Episode Statistics (HES) database in England.
Methods All in-patient finished consultant episodes of care (FCEs) relating to patients undergoing TAVI between 01/04/2009 and 31/03/2014 were extracted for the period from the TAVI admission until 1 year after the TAVI discharge date. FCEs were aggregated into admissions, and for each admission, administrative details, procedure codes, and diagnosis codes were analysed. This provided information on the dates of admission/discharge, the reason for admission (ICD-10 code), the urgency of admission, whether the admission was due to a complication, length of hospital stay, and the procedures undertaken during the admission (OPCS codes). The pseudonymised patient identifiers were extracted and linked with mortality data from the Office of National Statistics up to 31/03/2015. A Kaplan-Meier survival analysis of time to death was performed.
Results After exclusions, 4874 patients undergoing TAVI between 01/04/2009 and 31/03/2014 were identified from the HES database. During the TAVI admission, the median [interquartile range] length of stay was 8 [6:14] days, and 216 (4.43%) patients died. In the 30 day period following TAVI discharge, there were 1602 readmissions by 1205 patients (25.9% of patients surviving at TAVI discharge), accounting for 16399 days in hospital. This corresponded to a rate [95% CI] of 0.34 [0.33:0.36] readmissions and 3.52 [3.47:3.57] days in hospital per patient surviving at TAVI discharge (4658). In the 30 day to 1 year period following TAVI discharge, there were 6868 readmissions by 2500 patients (54.5% of patients surviving at 30 days post TAVI discharge), accounting for 53481 days in hospital. This corresponded to a rate [95% CI] of 1.50 [1.46:1.53] readmissions and 8.10 [8.01:8.17] days in hospital per patient surviving at 30 days post TAVI discharge (4584). 710 (14.6%) patients had died at one year post TAVI discharge. In the Kaplan-Meier survival analysis, median survival [95% CI] was reached at 1969 [1836:2136] days (5.4 years).
Discussion This is the first study to estimate the in-patient resource usage of a national cohort of TAVI patients in the NHS for the year following the procedure, and may provide useful reference information for commissioners and clinicians involved in decision making. The methods developed provide a technique of tracking patients longitudinally following procedures through the HES database to gather useful clinical information, and are generalisable to other procedures. A limitation is that the data are dependent on the accuracy of clinical coding, which has been shown to vary between providers.
Abstract no. 432 Designing a bedside application for adverse event reporting
Hanne Aserod and Ankica Babic, Department for Information Science and Media Studies, University of Bergen, Bergen, Norway
Introduction We present a mobile software application development for safety reporting within the field of angioplasty. The application aims at supporting physicians with capturing and retaining data regarding safety events. A combination of Interaction design and User experience techniques was used to inspire usability1 and create useful, intuitive interface. The consequence of not considering the user experience could be user frustration and the user looking for an alternative solutions to data capture. If f forced upon users, an application usage could increase the likelihood of mistakes increases, and reduce effectiveness.2
Method To collect data and define system requirements a literature review and a field study were conducted which resulted in both quantitative and qualitative data. The data was analyzed to understand the data flow and clinical processes all with a purpose to enable a user keeping in touch with the whole hospital information system. To be able to utilize the users’ skills and experiences within their domain, it was important to include them in the participatory design process. To get feedback on the concept, medical staff was given the screens together with explanation of the concept based on several levels of functionality.
Results Proposed user interface enables entry of data specific for adverse events of the knee and hip implants. Besides the patient data, the system allows entry of the event classification (serious, non-serious) and treatment, as well as the connection of the database maintained within the Helse Bergen hospital system. Reports could be initiated and retrieved if there are previous adverse event instances. Expert evaluation of the first design solution was performed using low fidelity prototype. It has shown that design was relevant, straightforward, done in a way that official reporting would commence. A question was also asked if the system could be adjusted to general reporting.
Discussion The design was met with enthusiasm by the healthcare professionals. However, it has been clear that there are reservations exist for reporting adverse events in general. The main reason seems to be a heavy work burden. There were also concerns about being viewed negatively by other medical staff. Attitudes towards reporting were not entirely negative, for example, the biomedical engineer lab that evaluates explanted medical devices would appreciate such a bed side reporting. Interviewed physicians accepted this point of view and did not entirely rule out their participation. Therefore, more work needs to be done to address attitudes towards reporting and lack of motivation for it.
Conclusion The development is directed towards the high-fidelity prototype and further web-based system development that will enable more detailed reports. Those will be fit into the hospital information system and provide basis for other functionalities such as e-learning and other general reporting.
References
N. Kellingley, “What is the difference between Interaction Design and UX Design?” [Online]. Available: https://www.interaction-design.org/literature/article/what-is-the-difference-between-interaction-design-and-ux-design. [Accessed: 07-May-2016].
O. Mival and D. Benyon, “Requirements Engineering for Digital Health,” A. S. Fricker, C. Thümmler, and A. Gavras, Eds. Cham: Springer International Publishing, 2015, pp. 117–131
Abstract no. 434 PITeS-TIiSS: complex chronic patient personalized decision support
Noa P. Cruz Díaz, Group of Research and Innovation in Biomedical Informatics, Biomedical Engineering and Health Economy. Institute of Biomedicine of Seville, IBiS / Virgen del Rocío University Hospital, Seville
Alicia Martínez-García, Virgen del Rocío University Hospital - FISEVI, SEVILLA
Maria Jose Garcia Lozano, Camas Primary Care Center, Seville
Bosco Barón Franco and Lourdes Moreno Gaviño, Internal Medicine and Integrated Care Department, Virgen del Rocío University Hospital., Seville
Carlos Parra, Group of Research and Innovation in Biomedical Informatics, Biomedical Engineering and Health Economy. Institute of Biomedicine of Seville, IBiS / Virgen del Rocío University Hospital, Sevilla
Complex Chronic Patient management is of great difficulty, in a context that requires personalization of actions based on the complexity of the patient’s condition over time. It needs to complement the recommendations defined in clinical guidelines from recommendations based on treatments performed on a representative set of patients, identifying conflicts between the recommendations of different guidelines designed for handling isolated chronic diseases. It also requires its extension to specific protocols in areas not described with sufficient detail in clinical guidelines in terms of safety and quality. The PITeS-TIiSS project aims to overcome this problem. Its main goal is to design and deploy a Clinical Decision Support System which helps to improve personalized decisions based on evidence and reduce variability in clinical practice in an integrated care domain. It will perform, integrated into the workflow of the healthcare professional, two types of recommendations related to the need to identify the duality between the best practice defined by consensus of domain experts and the analysis of the results obtained from patients with similar characteristics. From the review of the integrated care process of the pluripathological patient1 and the existing clinical practice guidelines on the management of acute and chronic heart failure2 and chronic obstructive pulmonary disease,3 it will be defined decision rules that allow applying, automatically and personalized to the patient’s conditions, clinical knowledge. It will also take into account cross-cutting tools such as the Stopp/Start4 and Less-Chron5 criteria as well as a prognostic scale called Profund index6. The process will be dynamic in order to improve its adaptation for changes in the reference knowledge and for the feedback on its use, introducing the concept of Learning Health System. In this study, the tool will access the information provided by the Health Information infrastructure of the Andalusian Public Healthcare Service. The integration of information will be carried out in a fast, consistent and reusable way. Final results will be reported in December 2018.
References
J.A. Mitchell, et al. 50 years of informatics research on decision support: What’s next. Methods of information in medicine, 50(6), 525. (2011).
P. Ponikowski, et al. 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. European heart journal. (2015).
M. Miravitlles, et al. Guía española de la EPOC (GesEPOC). Actualización 2014. Archivos de bronconeumologia, 50, 1-16. (2014).
D. O’mahony, et al. STOPP/START criteria for potentially inappropriate prescribing in older people: version 2. Age and ageing. (2014).
A. Rodríguez-Perez, et al. DI-060 Translating into english a new tool to guide deprescribing: A cross cultural adaptation. Eur J Hosp Pharm 2016, 23:A144-A145. (2016).
M. Bernabeu-Wittel, M. Ollero-Baturone, D. Nieto-Martín. Polypathological patients and prognostic scores. About the PROFUND index. Eur J Intern Med, 23(4):e116. (2012).
Abstract no. 438 Canadian primary care EMRs as the basis for a registry RCT.
Frank Sullivan, University of St Andrews, Scotland
Karim Keshavjee, Infoclin, Toronto
Braden OO’Neill, North York General Hospital, Toronto
Michelle Greiver and Frank Sullivan, University of Toronto, Toronto
Introduction Registry Randomized Controlled Trials (RRCTs) provide clinical researchers with the ability and resources to ask important clinical questions and design studies without the inherent biases often introduced through trials which recruit by other means. RRCTs have three key characteristics:
1. Randomly assigning patients in a clinical quality registry which combines the features of a prospective randomized trial with a large-scale clinical registry.
2. They are more pragmatic and enable fast enrolment, control of non-enrolled patients, and the possibility of very long-term follow-up.
3. The clinical registry can be used to identify patients for enrolment, perform randomization, collect baseline variables, and detect end points.
Method Qualitative work for a pilot and optimisation study which builds upon recent developments in Canadian Research Capacity:
The ability to extract, transform and link EMR data to administrative data for ascertainment of long-term outcomes in the $35M Canadian Institute for Health Research (CIHR) Funded Strategy For Patient Orientated Research(SPOR) Diabetes Action Canada research program.
Improvements in the quality of Primary Care EMR data in the Canadian Primary Care Sentinel Surveillance Network (CPCSSN):
The ability to link EMR data to trial management software (REDCap in this case)
Stronger research infrastructure in primary care in Practice based Research Networks (UTOPIAN, SAPCRen and NAPCRen in this case)
Results 21 of 60 practices surveyed are willing to engage in an RRCT involving randomising patients taking Metformin as the 1st line drug for Type 2 diabetes to Empaglifozin or continuing the biguanide. By the time of the presentation we will have conducted focus groups and interviews with patients, clinicians, Research Ethics Boards and policymakers in Ontario and Alberta.
Discussion Health Technology Assessment (HTA) Programme funded work in the UK by van Staa et al has shown that RRCTs using primary care EMRs is feasible but optimisation of the study in a pilot is required to address REB concerns and workflow issues. In particular the mechanisms to recruit sufficient patients and minimise the workload on physicians and clinic staff by approaching potentially eligible study subjects outwith consultations have been shown to be important.
Conclusion Before embarking on a substantive trial, recruiting the thousands of patients likely to be necessary for adequate power to answer this question effectively we are conducting a mixed methods feasibility and optimisation study. Experience in this T2DM example will be useful for the development of RRCTs on other topics in Canada and other countries where primary care EMRs provide access to data and Practice Based Research Networks enable studies to be implemented.
Abstract no. 441 Importing patient generated health data from wearable devices to multiple sclerosis quality register
Michael Fardis, Karolinska Institutet, Stockholm
Nabil Zary, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
Introduction Patient generated health data (PGHD) from Wearable Devices (WD), could transform Healthcare and be found useful in the management of quality of care and research of a chronic disease, like Multiple Sclerosis (MS). Objective: The study investigated what set of PGHD could be relevant for MS and explored challenges to the use of PGHD by Quality Registers (QR).
Methods Sequential explanatory mixed method design was chosen as a methodology. A questionnaire was delivered to MS professionals, in order to find the most meaningful PGHD for MS. Interviews followed, in order to assist the interpretation of the questionnaire’s findings.
Results 35 healthcare professionals answered the survey. Analysis of the questionnaire results offered a proposed dataset of PGHD for MS. The interviews revealed challenges (legislative, organizational) and opportunities (innovative solution) of tracking a disease like MS with the usage of WDs.
Conclusion Several challenges exist in the usage of PGHD. WDs alone cannot cover the measurements for a disease like MS and applications for patients’ reporting are needed as well.
Abstract no. 450 User-centred design of e-health technology for patients and professionals in productive teams - multidisciplinary work across organisational borders
Berglind Smaradottir, Santiago Martinez, and Rune Fensli, University of Agder, Kristiansand
Introduction The research project Patients and Professionals in Productive Teams (3P) 2016-2019, approved by the Research Council of Norway and funded through the Regional Hospital Trust Funds in Norway, aims to study multidisciplinary teamwork across organisational borders with a citizen-centred approach and focus on health service research, quality of care and patient safety. Health services face the challenge of providing individualised treatment to a growing ageing population prone to long-term conditions and multi-morbidities. There is an urgent need to understand how to operationalise patient-centred and integrated care, supported by technology where confidentiality, efficient team collaboration, quality of care and patient outcomes are at the centre. In this way, the 3P project includes 4 pilot sites that contribute to the development of innovative technology that is: 1) citizen-centred 2) coordinated, proactive and planned 3) having one-point of contact 4) using multidisciplinary teams and 5) a learning care system. The 3P project aims are to improve quality of care and patient safety for citizens with long-term conditions with the ultimate goal of improving outcomes, care experience and reducing costs.
Methods Based on user-centred design principles1,2 4 pilot sites in Norway and Denmark will work on functionality and organisational use of e-health support systems for care delivery. In the first project phase, field studies and focus group interviews will identify existing patient flows and map out the experienced current obstacles. The project will propose then coordinated optimal procedures to improve the existing workflows with the patient at the centre and with one-point-of contact with the organisational healthcare professional team. In the next phase, workshops with end-user groups will define functional requirements for an e-health technology support system, outline ideal teamwork situations and use-cases with the focus on access to and sharing of information. The end-users will describe advantages of ideal procedures and identify how e-health systems can provide support for efficient team collaboration.
Results The 3P project is in the first project phase. The preliminary results from data collected during field studies and focus group interviews in the 4 pilot sites will be published at the end of 2017.
Conclusion This research study contributes to understand the role of e-health technology that supports multidisciplinary work in several ways. Firstly, it contributes to the knowledge on patient-centred systems, aiming at increasing efficiency and quality of care and empowering citizens with long-term conditions. Secondly, it shows how to actively and effectively involving end-users in design and development of e-health technology when conducting empirical research. Thirdly, it provides recommendations for large scale deployment of e-health technology that supports multidisciplinary teamwork across organisational borders.
References
B. Smaradottir, User-centred Design and Evaluation of Health Information Technology, Doctoral Dissertation, University of Agder, Norway, 2016. ISBN 978-82-7117-830-7
B. Smaradottir, M. Gerdes, S. Martinez, R. Fensli, The EU-project United4Health: User-centred design of an information system for a Norwegian telemedicine service, J Telemed Telecare, Vol. 22(7) (2016), 422–429. DOI:10.1177/1357633X15615048
Abstract no. 453 Extracting social factors from clinical free text
Andika Yudha Utomo and Azad Dehghan, University of Manchester, Manchester
Tom Liptrot, Daniel Tibble, Matthew Barker-Hewitt, and Goran Nenadic, The Christie NHS Foundation Trust, Manchester
Introduction Electronic health records (EHRs) contain vast amount of clinical information, with free text clinical narratives used as a routine medium to record details of patient care. The use of Text Mining (TM) methods in the clinical domain has therefore become increasingly important in order to extract knowledge from large volumes of EHRs. Social factors, such as smoking or alcohol consumption, in particular have repeatedly been shown as an important factor in modeling clinical outcomes. Such information is often recorded only in clinical narrative. Here we present an algorithm to automatically predict a patient’s smoking status from retrospective textual clinical records.
Method We developed a set of rule-based methods and embedded them in the GATE (General Architecture for Text Engineering) framework. The system takes as input a set of free text notes and automatically assigns the smoking status (current-, past-, non-smoker, smoker, unknown) to the given patient. The rules were crafted based on several lexical cues (e.g. mentions of smoking-related terms, including, for example, the number of cigarettes smoked), along with information on word distance, negation, and context. A dictionary-based post-processing component was developed to prevent obvious false positives. The methods described here are available as open source software at: http://github.com/christie-nhs-data-science/social-factors-classifier
Results To explore how general the rules are, the system was evaluated on three manually annotated datasets coming from: Informatics for Integrating Biology and the Bedside (i2b2) 2006 smoking challenge (i2b2/2006), i2b2/2014 heart disease risk factors identification challenge, and The Christie NHS Foundation Trust clinical notes (Christie). The results (see Table below) show that the method achieved a high-level of performance by reaching 0.90 score for micro-averaged F1-measure.
Discussion The scores are comparable to the best submissions on both the i2b2 2006 and 2014 challenges, demonstrating that the system is robust and applicable to clinical narrative coming from different healthcare disciplines. The main challenge proved to be the identification of the generic class of smoker, given the label is ambiguous (it does not discriminate between current/past smokers) and there are only small number of records with this label in each dataset. Similarly, some expressions (e.g. “He has a smoking history”) are ambiguous and it is difficult to differentiate whether such expressions refer to a current or past smoker (or it should be just smoker).
Conclusion We have demonstrated that a rule-based system can be used to extract social factors from clinical narratives with high performance. While most of social factors are expressed explicitly in text, we will use language modelling on larger datasets to determine implicit patterns that may indicate the presence of a targeted social factor.
Abstract no. 465 Validation of a case definition for depression in administrative data using a chart review reference standard
Chelsea Doktorchik, Cathy Eastwood, Mingkai Peng, and Hude Quan, University of Calgary, Calgary
Introduction Because the collection of mental health information through interviews is expensive and time consuming, interest in using population-based administrative health data to conduct research on depression has increased. However, there is legitimate concern that misclassification of disease diagnosis in the underlying data might bias the results. Our objective was to determine the validity of ICD-9 and ICD-10 administrative health data case definitions for depression using a review of family physician (FP) charts as the reference standard.
Methods Five trained chart reviewers reviewed 3362 randomly selected charts from years 2001 and 2004 at 64 FP clinics in Alberta and British Columbia, Canada. Depression was defined as presence of either: 1) documentation of major depressive episode, or 2) documentation of specific antidepressant medication prescription plus recorded depressed mood. Bipolar depression and alternate indications for antidepressants were excluded. The charts were linked to administrative data (hospital discharge abstracts and physician claims data) by unique personal health number. Validity indices were estimated for six administrative data definitions of depression using three years of administrative data.
Results Depression prevalence by chart review was 15.9%-19.2% depending on year, region, and province. An ICD administrative data definition of ‘2 depression claims within a one-year window OR 1 discharge abstract data (DAD) diagnosis’ had the highest overall validity, with estimates being 61.4% for sensitivity, 94.3% for specificity, 69.7% for positive predictive value, and 92.0% for negative predictive value. Stratification of the validity for this case definition showed that sensitivity was fairly consistent across groups, however the positive predictive value was significantly higher in 2004 data compared to 2001 data (78.8% and 59.6%, respectively), and in Alberta data compared to British Columbia data (79.8% and 61.7%, respectively).
Discussion Our estimates of validity indices are similar to those reported in the literature. Sensitivity is often moderate, and specificity is often high, as depression is a difficult mood disorder to diagnose due to its varying severity and presentation. Limitations to this study include the use of FP chart data as the “gold standard”, given the potential for missed or incorrect depression diagnoses.
Conclusions These results suggest that administrative data depression case definitions are moderately valid, and that administrative data can be used as a source of information for both research and health services purposes.
Abstract no. 466 Implementation of mHealth interventions in low income settings: overcoming the risk for prioritising up-scaling over evidence
Mathew Mndeme, Hamish Fraser, and Tolib Mirzoev, University of Leeds, Leeds
Susan Clamp, Leeds Institute of Health Sciences, University of Leeds, Leeds
Background Use of mobile phones for health innovations (mHealth) is increasingly adopted in Tanzania to address different health-system constraints. This builds on the advantage of a wide penetration of mobile phone networks (79%), rapid growth of teledensity (85%), and successful experiences of innovations for mobile-money transactions. This study evaluates the implementation experience of mHealth intervention for communicable diseases reporting and outbreaks notification in Tanzania (eIDSR). It identifies implementation practices that led to prioritise up-scaling over evidence. Alternative, evidenced-based approaches for scaling up is proposed.
Methods This was a retrospective mixed-methods study in two districts. Data were collected through (1) semi-structured interviews, (2) analysis of eIDSR implementation documents, and (3) analysing trends of reporting through the eIDSR. Data from (1 & 2) were thematically analysed and (3) were analysed using descriptive statistics.
Results eIDSR was implemented under coordination of the Ministry of Health using a top-down approach with minimal involvement of users were data originates. Its inception was supported by development partners with interest in diseases surveillance. They provided resources for piloting and up-scaling. Funding came from different sources, some of which dictated a deployment approach. eIDSR was piloted in one district (out of 182) for 2 months and up-scaled to cover 70 districts (38.5%) within 2 years. Despite being integrated to the mainstream Health Information system (HIS) strategy, it was not regarded as a reliable source of surveillance and operated parallel to paper-based system. During the 2015-2016 cholera outbreak, eIDSR failed to support reporting and notification of cases as anticipated. Conversely, unstructured SMS and phone calls were the prefered means of reporting. Implementation design did not envision contextual situations like designating of cholera treatment centres as source of data. Additionally, eIDSR was hardly used due to unresolved technical challenges and use of personal mobile phones for reporting, among other reasons. As a result, only 65 out of 3,608 (2%) cases and deaths were reported through eIDSR.
Discussion There was weak evidence to support the level of up-scaling the eIDSR intervention. Most of the implementation challenges observed during piloting stage were still obvious throughout the up-scaling process. The urgency of addressing the burden of communicable-diseases and controlling outbreaks, uncertainty of funding sources, and reported potentials of mHealth innovations to improve health, temped implementers to accelerate up-scaling without thorough evaluation of implementation practices and evidence of intended outcomes.
Conclusion This study suggests that the risk of prioritising up-scaling of mHealth over evidence is associated with both inhibiting factors (unreliability of funding sources, and lack of evaluation studies) and facilitating factors (availability of mHealth supportive infrastructure, successful experience of using mobile phones for money transactions, and technological pressure). There is a need for rigorous evaluation studies to help inform mHealth implementation designs and practices. mHealth implementation should be funded within the mainstream eHealth strategies and efforts should be directed to overcome the weakness of conventional HIS practices to accommodate mHealth innovations. Similarly, technical integration of mHealth into mainstream HIS strategy should take place concurrently with revisiting of non-technical determinants of HIS performances.
Abstract no. 467 Blood pressure data quality assessment in Canadian and UK EMR data: how is blood pressure recorded?
Mingkai Peng, Shiva Wagle, Hude Quan, Tyler Williamson, and Guanmin Chen, University of Calgary, Calgary
Background The electronic medical record (EMR) provides rich clinical information on patients and an opportunity to conduct risk factor surveillance at population levels. In this study, we investigated the data quality of blood pressure (BP) in two large EMR databases in UK and Canada.
Methods This is a population-based cross-sectional study. We identified the active patients in The Health Improvement Network (THIN) and Canadian Primary Care Sentinel Surveillance Network (CPCSSN) from 2008 to 2010. The blood pressure measurements were extracted from the database and stratified by age and sex. We examined the proportion of patients with BP measurements. The time gap between measurements was examined for patients with more than 1 measurement. The context information of blood pressure, such as diagnosis and prescriptions, was also examined.
Results In THIN, there were 3 027 635 individuals. The proportion of individuals with BP recorded during 2010, 2009-2010 and 2008-2010 were 38.6%, 51.6%, and 59.4%, respectively. Females had more BP recorded than males. The proportion of individuals with BP measured increased with the age. In the period of 2008 to 2010, there were 42.4% of individuals with at least 2 BP measurements? Among those individuals with at least 2 BP records, 19.7 % of individuals has two BP measured within 1 month. Analysis on CPCSSN is still underway.
Conclusion The EMR contains rich BP information, especially for the female and older individuals. The number of individuals with BP measurements and the number of BP measurements increased with increased observational periods.
Abstract no. 471 Datasets of interest to researchers studying UK primary care and related topics
Lucy McDonnell, King’s College London, London
Frank Sullivan, University of Toronto, Toronto
Brendan Delaney, Imperial College, London
Introduction Primary care in the U.K. generates an extraordinary amount of data creating unrivalled opportunities for research. Some of this data however, is underused, unknown and historically only accessed by certain institutions or experienced researchers. The aims of this project were to systematically identify, describe and facilitate access to primary care datasets in England, Scotland, Wales and Northern Ireland and to present the information found in an accessible format for all researchers.
Method We searched for relevant datasets via a variety of methods. Methods included reviewing previously compiled but unpublished lists of datasets, searching the internet using the search terms ‘primary care’ ‘general practice’ ‘GP’ ‘data’ and ‘datasets’ and identifying datasets which use primary care electronic record systems e.g. EMIS and Vision to extract data. Metadata were mostly taken from comprehensive website searches. Completed metadata templates were sent out via email to data custodians to verify the information prior to publication.
Results We identified more than 30 datasets divided into the following categories: electronic medical record data, quality of primary care services, prescribing data, audit, health surveys, special datasets, cohort studies, administrative datasets and screening datasets. The largest datasets were electronic medical record data linked to primary care electronic medical system e.g. ResearchOne incorporating over 30 million registered patients. The smallest datasets were audits and surveys, e.g. the health survey for Northern Ireland which included around 4000 patients in 2014/5. Metadata included type of data, context of data and method of extraction, coverage, geography, duration, volume, events for data collected, granularity, coding, consent and access. Example publications using the data were included for the majority of datasets listed. The majority of dataset custodians replied to our enquiry and verified the information.
Discussion Our search identified a large number of datasets which may be useful to primary care researchers including major popular datasets, and less well known resources. Identifying peer reviewed publications which have used the datasets allowed us to highlight the variety of research that can be carried out with routinely collected data. We used a template similar to that on the Health Data Finder website to ensure consistency of metadata.
Conclusion This project fills a gap in the literature regarding the existence and availability of primary health care datasets and is intended to be used as a starting point for researchers. It is a useful resource for both established primary care researchers who have current or historic links to particular datasets, as well as potential users who may not be aware of the existence of such a large variety of datasets or have previously encountered barriers to access.
Abstract no. 481 Physiodom HDIM: good health, good ageing
Maite Franco, Silvia Sánchez, and Esther Jovell, Consorci Sanitari de Terrassa, Terrassa
Healthy ageing depends on genetic, environmental and behavioural factors, as well as broader environmental and socioeconomic determinants. Some of these factors are within the control of the individual, usually referred to as lifestyle factors, and others are outside the individual’s control. Social determinants of health, such income and education, influence the choices that individuals can make and create life circumstances which limit opportunities for healthy lifestyle and create health inequalities. Engaging in appropriate physical activity, healthy eating, not smoking and using alcohol and medications wisely in old age can prevent disease and functional decline, extend longevity and enhance ones quality of life. There is evidence to suggest that the determinants of health are good predictors of how well both individuals and populations age. Physiodom HDIM intervention consists of a telemonitoring system which will be employed by participants at home to perform automatic measurement of weight, BMI, L/F ratio, blood pressure, manual measurements will be provided in physical activity. Also participants will be monitored by professionals around appetite, nutritional status and dietary intake.
Objectives To demonstrate favourable effects of Physiodom HDIM on nutritional status.
To validate that this new system is able to optimize the use of resource in different health-social care settings. To assess the impact of implementing Physiodom HDIM intervention in the existing services at different organisations.
To assess the usability of a remote home services, offering services such a personal calendar management, electronic messaging and remote monitoring of the delivery of home services, though the home TC monitor and health care professionals PC. To perform a process evaluation including the usability, feasibility, acceptability and implementation fidelity of the Physiodom HDIM intervention among the elderly and involved care-givers.
Abstract no. 483 Development of a national core dataset for the Iranian breast cancer multidisciplinary registry
Mohsen Goli, Najme Nazeri, Sara Dorri, Ebrahim Abbasi, and Alireza Atashi, Cancer Informafics Department, Breast Cancer Research Center, ACECR, Tehran, Iran., Tehran
Keivan Majidzadeh and Shahpar Haghighat, Breast Cancer Research Center, ACECR, Tehran, Iran., Tehran
Introduction Different disciplines in multiple research centres need to collect a large amount of various data, which commonly spends a lot of cost and time. Normative collection of necessary data in every field will increase speed and precision of research. Regard of registry Importance, the researchers tried to create a maximum comprehensive dataset for a multidisciplinary registry for breast cancer Iranian Breast Cancer Patients.
Methods First, the scientific literature and published studies were systematically reviewed and their related data elements were extracted. The results were presented to specialists in different disciplines to give their comments. The next phase was the survey of experts on a three-step Delphi, which had an expert panel in the last step. Finally, a combination of scientific experts and scientific literature review was used.
Result In the first step, 194 component (criteria) in 11 specialized (specific) breast cancer-related fields were extracted and sent to experts. In the first Delphi step the experts added 37 components, 11 elements were duplicates and not particularly helpful. Finally, the maximum dataset was confirmed by members of the Expert Panel with 220 independent data elements in 11 specialized multidisciplinary fields in breast cancer.
Conclusions The results consensus with Delphi method and expert Panel, was more useful for the Iranian breast cancer research centers because it was collected for their objectives and it will help to increase efficiency in similar centers in storage and retrieval of their data.
Abstract no. 485 Measuring quality of teleconsultation services from the patients’ perspective: development of a questionnaire
Leonie Thijssing, Esmée Tensen, Frank Horenberg and Monique Jaspers, Academic Medical Center- University of Amsterdam, Amsterdam
Introduction Healthcare provided through teleconsultation has proven to be as efficient and effective as face-to-face consultation with high diagnostics accuracy. Literature about patient satisfaction with teleconsultation generally shows a positive trend. It does however not provide insight into quality aspects of teleconsultation that contribute or impede patients’ acceptance of these services. The aim of this research is to develop a validated and standardized questionnaire to measure experienced quality of teleconsultation services from the patients’ perspective.
Method The methods used to develop the questionnaire were based on the CQI methodology. Following this methodology we first conducted a systematic literature study and focus groups with patients, both to acquire quality aspects of teleconsultation services patients perceive as important. Search terms in PubMed and PsychINFO related to the categories: teleconsultation services, patients’ perspective, and quality of healthcare. Revealed quality aspects were transformed into a draft questionnaire which was pre-validated among stakeholders: patients, GPs, specialists, and the teleconsultation provider. In the second phase two unstructured cognitive interview rounds with patients were conducted to test the reliability and comprehensiveness of the questionnaire. Patients in the focus groups and cognitive interviews experienced a teledermatology, telepulmonology or telecardiology consultation, were 18 years or older, and were invited after permission of the GP. Problems encountered with the questions were coded according to the QAS-99 methodology and, if necessary, rephrased.
Results The systematic literature review resulted in 1474 publications. After exclusion by abstracts and full-text analysis, 7 publications remained from which 20 quality aspects of teleconsultation services deemed important by patients, were derived. In the focus groups five (first) and six (second) patients came up with 22 quality aspects perceived as important of teleconsultations. Five quality aspects were similar to those in the review, resulting in a total list of 37 unique quality aspects which were mapped to nine CQI themes: Access to care (4), Communication and information (6), Patient management role (6), Competence (3), Organization of care (8), Costs and compensations (5), Effective and safe care (2), Continuity of care (2), Interpersonal conduct (1). The cognitive interview rounds revealed problems in 35/42 and 25/42 questions respectively. After these interviews, a total of 37 questions and/or their answer categories were reformulated, one question was removed and one question was added.
Discussion We aimed at developing a questionnaire to measure the quality of care delivered through teleconsultations as perceived by patients. We identified quality aspects using a systematic literature study and focus groups, developed a concept questionnaire which was pre-validated among different stakeholders, and conducted cognitive interviews to assess the reliability and comprehensiveness of the questionnaire. In future research, we will test the reliability, internal cohesion, and the psychometric power of the questionnaire.
Conclusion The questionnaire is essential to gain insight in patients’ experiences and perspectives on teleconsultation and can improve patient satisfaction with teleconsultation. Additionally, the questionnaire could be used by healthcare centers, patients, public health inspection and government to improve the quality of teleconsultation services, to decide about reimbursement and to measure and monitor quality of care.
Abstract no. 486 Perceptions of barriers and facilitators to the sharing and linkage of health related data - how the DASSL model responds
Rosalyn Moran, Health Research Board, Dublin 2
Introduction The paper will present results of research which examined the barriers and facilitators which impact on researchers’ and other data users’ ability to access, share and link data in Ireland. The work led to the development of the Data Access, Storage, Sharing and Linkage or DASSL Model which can enable safe access and linkage of sensitive data.
Method Interviews were held with 59 informants. Included were researchers from the public health and health services research data communities, actors involved in the collection, use, sharing and linkage of data in Ireland from a range of sectors and government departments. International best practice in the generation of solutions for safe data use and linkage were examined and their fit to the legislative, cultural, technical environment in Ireland was assessed.
Results Results of the interviews conducted and international experience are reflected in the DASSL model. The model comprises seven elements – five related to infrastructure and services [a health research data hub, safe haven, trusted third party and data linkage service, output checking and disclosure control and a research support unit] required for safeguarding data, and two related to the broad legislative and socio-cultural context needed to facilitate implementation of the model i.e. governance and public engagement.
Discussion The proposals put forward for creating a safe environment for access, sharing and linkage of research and related data need to be considered by all key stakeholders. Implementation of the DASSL model will allow for safe usage of currently under-exploited data which can help inform health and wellbeing but also serve national economic and social agendas.
Conclusion It is argued that if we want a safe and trusted modern infrastructure that will enable researchers to unlock the significant value of currently underexploited data for the public good, then the DASSL model or a similar model needs to be implemented in Ireland.
Abstract no. 487 Differential impact on presentations types and equity following the relocation of a metropolitan emergency department.
Mei Ruu Kok, Matthew Tuson, Bryan Boruff, Alistair Vickery, and David Whyatt, University of Western Australia, Perth
Introduction The closure of a coastal emergency department (ED) and the opening of a new inland site in metropolitan Perth, Western Australia, was expected to improve overall access to ED. The objective of this study was to examine the impact of ED relocation on different types of ED presentations.
Methods To address this aim, ED presentations were first divided into urgent/non-urgent medical and urgent/non-urgent trauma (injuries and poisoning) based on triage categorisation and ICD-10 coding. The ED relocation occurred in February 2015. Each SA3 regions was modelled separately, comparing February to October 2014 to the same period in 2015, after adjusting for population. Estimates of the burden of ED utilisation attributable to ‘distance to ED’, were obtained using separate Poisson regression models for adults and children. Confidence intervals were estimated using a stratified bootstrap approach, at the 95% significant level.
Results 13% of the entire population had their travel distance to the nearest ED decreased by at least 1km but on average 5km, while 5% of the population had their distance increased by at least 1km and on average 5km. The total number of ED presentations increased 7.1% within the region, with population growth of 3.6%. Areas near the new ED saw a significant increase in urgent and non-urgent medical presentations in adults. Change in distance contributed 30% to 70% of the increased in urgent medical presentations. The increase of non-urgent medical presentations attributed to change in distance varied between 20% and 40%. Conversely, significant decreases in both urgent and non-urgent medical presentations were observed in areas near the closing ED, with more than 200 fewer presentations in each category. For urgent medical presentations, these decreases were entirely attributable to the increased distance to ED. The increase in urgent and non-urgent trauma presentations attributed to decreased distance ranged from 9 to 15% and 15% to 30%, respectively.
Discussion Travel distance to ED has a greater impact on medical presentations than trauma presentations. Surprisingly, distance impacted strongly on urgent medical presentations. It is anticipated that urgent medical presentations are non-trivial events. However, there was a significant decrease in the proportion of urgent medical presentations being admitted in areas near the new ED, suggesting that possibly “inappropriate” use of ED increases with the greater convenience and accessibility to the ED service. No change in admission rates amongst urgent medical presentations was observed in areas close to the closed ED. Therefore, our results suggest that inequity existed both before and after the relocation of the ED. While overall access to ED increased with the opening of the new facility, new areas of inequity may have been created.
Conclusion The relocation of an ED has varying impact on different types of ED presentations in localised areas. The extent of this impact cannot be assessed using overall measures of ED utilisation or accessibility. Localised impacts on urgent medical presentations suggest that equity of access cannot be easily addressed by relocation of centralised services.
Abstract no. 490 SEAMPAT: Evaluation of the patient-application to improve continuity of medication use through patient engagement: preliminary results
Sophie Marien, Delphine Legrand, and Anne Spinewine, Université catholique de Louvain, Louvain Drug Research Institute, Clinical pharmacy research group (LDRI/CLIP), Bruxelles
Catherine Forget and Pierre Pagazc, Université de Namur, Research Centre in Information, Law and Society (CRIDS), Namur
Patrick Heymans, Université de Namur, Research Centre in Information system engineering (PReCISE), Namur
Ravi Ramdoyal and Valéry Ramon, Centre d’Excellence en Technologies de l’Information et de la Communication (CETIC), Charleroi
Introduction Continuity of medication management is a worldwide patient safety concern requiring information sharing among providers, patients, and families across different settings. Discrepancies consist of unexplained medication discontinuity and are a threat to patient safety. To improve continuity of care, patient engagement and the use of information technology (IT) are recognized as promising solutions. The SEAMPAT project was performed in the Walloon Region in Belgium and designed as a proof-of-concept approach. Its objective is to define and implement an electronic medication reconciliation process in which the patient is actively involved. Two secured applications are being developed: the patient-application and the medication reconciliation application. Those are linked to the Regional eHealth Network. We started with low-fidelity prototypes and moved to high-fidelity prototypes through Plan-Do-Study-Act cycles to improve the tool and adapt it to users’ needs and workflow. The present abstract focuses on the high-fidelity prototype of the patient-application.
Method Starting from information communicated on the Regional eHealth Network by the general practitioner (GP) and/or the hospital, the patient validates his/her medication list through the patient-application. This patient’s list can then be accessed by the GP and other physicians. We perform a three month (September - December 2016) prospective observational study including 48 patients from the Region of Namur. Patients, recruited through both general practices and hospital clinics, volunteered to participate. The purpose of this study is to 1) assess the accuracy and completeness of the medication history collected, 2) evaluate usability including patient satisfaction - using the System Usability Scale (SUS) - and additional questions2, 3) explore the influence of the patient-application on patient participation – using the patient activation measure (PAM)3 – concerning his medication plan. The first set of evaluation was performed straight after the kick-off session for each patient.
Results Half of the participants (52%) are at least 65 years old. The majority are men (67% = 32). Participants take an average of 9 daily medications. More than half take at least one over-the-counter medication. A quarter (25%) of the patients can be considered as actively participating in their care (PAM level 4). After the first session, most participants (75%) reported that the patient-application is easy to use. They filled out an average SUS-score of 69%. Participants are convinced the patient-application could help their physician saving time to update their medication list. A large majority (92%) think it could help to improve communication and reduce medication errors. Participants familiar with technology described the patient-application as user-friendly and 81% of participants would advise a friend to use the patient-application. Accuracy and completeness of medication lists generated using the application will be measured in December when patients will be more familiar with the patient-application.
Discussion and Conclusion Preliminary results on the patient-application are promising with respect to usability. Further evaluation will be performed in next December.
Abstract no. 491 A methodology for optimising spatial accessibility to inform rationalisation of specialist health services.
Catherine Smith and Andrew Hayward, UCL, London
Introduction In an era of budget constraints for the National Health Service, strategies for provision of services that save costs without sacrificing quality are highly valued. A proposed means to achieve this is consolidation of services into fewer specialist centres. Potential benefits include increased levels of expertise reduced variation in quality, and simplification of care networks. A drawback is that it may reduce accessibility of services, particularly for diseases such as tuberculosis which may require multiple weekly clinic visits. One measure of spatial accessibility is the time that it takes to travel between locations. The aim of this study was to use travel time data to investigate the effects of service rationalisation on spatial accessibility through the exemplar of tuberculosis clinics in London.
Methods We extracted the residential locations of tuberculosis patients notified in London between 2010 and 2013 from the Enhanced Tuberculosis Surveillance system. We estimated travel times to each of 29 tuberculosis clinics in London using the Transport for London Journey Planner service, accessed via its Application Programming Interface. We determined impacts on travel times if patients were assigned to clinics based on minimum travel time as opposed to pre-existing commissioning arrangements. To investigate the impacts of service rationalisation on travel time, we determined optimum configurations of clinics for each possible subset of clinics (one to 28). We used a combinatorial optimisation algorithm to identify the set of clinics that provided the shortest overall travel time for a random sample of 1,000 patients.
Results This study was based on 12,061 tuberculosis patient residential locations. Mean travel time to clinics used by patients was 33 minutes (standard deviation 15.1 minutes), significantly longer than to nearest available clinics (27 minutes, standard deviation 9.6 minutes, t-test p<0.01). A total of 7,337 (61%) patients used their nearest clinic 2,130 (18%) used a clinic more than 15 minutes further than their nearest clinic, and 767 (6%) more than 30 minutes. Using optimum combinations of clinic locations, and assuming that patients attended their nearest clinics, a mean travel time of less than 45 minutes could be achieved with three clinics of 34 minutes with ten clinics, and of less than 30 minutes with 18 clinics.
Discussion This study shows that, in a major urban conurbation with large numbers of clinics treating the same condition, specialist services may be rationalised without impacting spatial accessibility. In London, current mean travel times for tuberculosis patients to clinics could be achieved with an optimum combination of around ten of the 29 clinics in London, provided that patients used their nearest clinic. Limitations of this study included use of estimated rather than actual travel times and the assumption that patients have a preference for clinics with shorter journeys from their place of residence.
Conclusion We have developed a methodological approach to optimise selection of clinic locations in the context of service rationalisation. In urban conurbations this may allow increased efficiency and quality of specialist services without substantially affecting spatial accessibility.
Abstract no. 493 Harnessing publicly available data to promote quality improvement: the evolution of the AF and AF-related stroke data landscape tool
Andi Orlowski, Ruth Slater and Jane Macdonald, Greater Manchester Academic Health Science Network, Manchester
Introduction In collaboration with Public Health England’s (PHE) initiative to improve the management of Atrial Fibrillation (AF) and reduce the number of avoidable AF-related strokes, the Greater Manchester (GM) Academic Health Science Network (AHSN) is harnessing insights from publicly available data across the AF care pathway. The initiative is aligned with NICE clinical guidelines (CG180, 2014), which outline best practice to improve standards of care in AF, including anticoagulation for patients at high risk of stroke. Publicly available datasets capture different segments of the AF care pathway through a range of reporting routes and at different time points – from primary care identification and management of AF, to secondary care and emergency admissions for AF-related stroke.
Methods GM AHSN has developed a first-of-its-kind online data capture and visualisation platform in AF that effectively integrates these datasets to gain the most complete evidential picture to-date across the care pathway. This includes up-to-date Quality Outcomes Framework (QOF) and GRASP-AF reporting, and Hospital Episode Statistics (HES) and Sentinel Stroke National Audit Programme (SSNAP) data. The AF- and AF-related Stroke Data Landscape Tool incorporates data from all 15 AHSNs in the UK and their memberships, enabling each CCG to pinpoint local opportunities for improved AF diagnosis and/or anticoagulation. As reporting cycles of these key datasets are not synchronised with one another, the AF Landscape Tool has been designed to easily incorporate new data releases on either a rolling or an ad hoc basis.
Results Based on insights collected from these datasets, each AHSN is able to support the creation of specific business cases for the improvement of AF care in their regions. For example, the GM AHSN has created a business case for better anticoagulation in high-risk patients with AF, which predicts a return on investment within 2 years and a potential reduction in avoidable AF-related strokes in GM by 365 in 2017. GM AHSN has also supported its member organisations to build successful business cases for AF screening programmes or treatment reviews, targeted to CCGs or individual practices that have a demonstrable need. Once piloted, successful initiatives can be scaled up to regional, and potentially national outreach.
Discussion As part of a comprehensive PHE and AHSN AF stroke-reduction initiative, the Landscape Tool helps to ensure resources are directed where they are most needed and enable maximum returns on investment. Version 2.0 of the AF Landscape Tool is planned for release in 2017, and will focus on more easily sharable and customisable interfaces to enable broader access and uptake, as well as looking at ways to integrate other relevant datasets. Updates to the Tool will also rapidly capture any changes in key datasets, including new QOF indicators, to track progress against PHE’s goal of preventing 5000 AF-related strokes per year.
Abstract no. 495 Challenges and results with the record linkage of Austrian health insurance data of different sources
Barbara Glock, dwh GmbH, simulation services, Vienna
Florian Endel, TU Wien, Institute for Analysis and Scientific Computing, Vienna
Gottfried Endel, Head Association of the Austrian Social insurance companies, Vienna
Niki Popper, dexhelpp, Vienna
Introduction Due to data privacy issues, routinely collected data of different sources is pseudonymized (e.g. MBDS minimum basic dataset from the Federal Ministry of Health, which up to 2015 does not have a personal identifier). This makes statistical analysis for decision support and health care planning very difficult. Data from insurance carriers (FoKo) is event based: whenever a hospital reports, a new data entry is generated. To enable efficient, significant and quality assured data analysis for patient centred assertions record linkage of these episodes is required with the aim of finding a personal ID for each MBDS episode.
Method For historical data (GAP-DRG1) a linkage has been done before. Some challenges remain for new data (GAP-DRG2): in MBDS data for the whole of Austria is available, but in FoKo only data for persons insured by the Lower Austria sickness fund a hospital stay may be split off in more data entries, due to intermediate reporting from the hospital. In GAP-DRG2 an iterative deterministic record linkage is applied: (1) determine the quality assured matching variables, (2) determine the minimum set of matching variables (MVs), (3) base match: check for unique matches in three basic MVs, (4) start level 1 match: data entries need to be identical in all MVs except 1 (MVs are varied Step1: missing Step 2: contradicting). (5) – (9) The same procedure is done for up to 6 missing/contradicting MVs. (10) Start the iterative process with remaining episodes at (4).
Results In GAP-DRG2 1.410.165 episodes from FoKo and 1.272.813 episodes from MBDS are designated to be matched. In the basematch 611.591 (48,05%) episodes could be matched. In the first iteration, a total of 1.271.395 (99,88%) episodes are matched, where most of them are found in level 3 (3 MVs are allowed to be NULL or contradicting, most of them involving the episode’s identifier). Finally, after iteration 3 a total of 1.272.104 (99,94%) matched data entries are found.
Discussion The main innovations of this procedure include significant improvement of previously developed methods, mainly concerning reproducibility, stability and adaptability to new data and documentation on every single step of the linkage procedure, allowing researchers to comprehend the origin of a link and adapt their data analysis strategies. Checks if the registered age differs with +/- one year is included and quality checks on the base match showed that age and district differs a lot. The procedure achieves the best possible outcome for the new datasets and is highly suitable to be used within new data. Further evaluations will be included by the end of the year.
Conclusion The applied determinist record linkage provided a rate of 99,94% matches. It is a further developed, structured and improved implementation of the historic matching. As soon as new data is incorporated the same procedure can be applied to new data, for more recent years or the whole of Austria. This project is part of the K-Project dexhelpp in COMET – Competence Centers for Excellent Technologies that is funded by BMVIT, BMWGJ and transacted by FFG.
Abstract no. 497 Using consensus clustering and resampling to identify stable subclasses of disease
Allan Tucker, Brunel University, London
Pietro Bosoni and Riccardo Bellazzi, University of Pavia, Pavia
Svetlana Nihtyanova and Christopher Denton, University College London Medical School, Royal Free Hospital, London
Introduction Different diseases can affect people in different ways: Firstly, disease categories are often “umbrella” terms for a group of subcategories of disease. Take Systemic Sclerosis (SSc), which is a chronic connective tissue disorder, affecting the skin, peripheral circulation and multiple internal organs. It can be classified into two subsets - limited cutaneous SSc, where skin thickness affects only areas distal to elbows and knees and diffuse cutaneous SSc, where skin involvement can affect the whole body. Of course, these are unlikely to be the only subcategories and discovering others will be essential if we are to make more informed diagnoses. Secondly, people respond in different ways to the same disease. For example, in SSc some patients are more affected with complications in the lungs, whilst others are in the kidneys, heart or gastro-intestinal system. Patients undergo regular assessments, including physical examination and a range of blood and internal organ tests. These tests can be used as predictors of organ complication / mortality. Systemic sclerosis shares traits that are common to many diseases: Variability in progression between different individuals, including subclasses of disease that can inform how an individual will progress, along with the eventual progression to an advance stage. If we can identify the different subclasses of disease along with the cohorts of patients that belong to them, then we can improve diagnosis, as well as build models that are more tailored to smaller groups of individuals to better manage disease progression.
Methods We are exploring the use of classification methods to predict different disease outcomes using a combination of clinical indicators related to SSc. We use unsupervised methods to pre-process patients into different cohorts and identify variations in symptoms. In particular, we explore how consensus clustering can be used in conjunction with bootstrap resampling to identify more stable and representative subclasses of disease. These clusters are used to improve models for disease classification (interpretability and accuracy). We assess how predictive the similarity of different bootstrapped clusters are in identifying stable underlying disease subclasses using the weighted Kappa metric.
Results Preliminary results on openly available diabetes data (from the UCI ML repository) and data collected at UCL’s Royal Free Hospital London, indicate that metrics such as weighted Kappa can be used on clusters generated from resamples to indicate how accurately these subgroups of patients reflect true underlying classes and subclasses of disease (correlations of 0.73 for diabetes data and 0.67 for SSc data). What is more, robust and consensus clusters are shown to generate more predictive models for diagnosing patients (improvements from 60% to 62% for diabetes data, and from 62% to 72% for SSc data).
Conclusions If we can successfully identify subclasses of disease, then we can better tailor our models for diagnosing disease and predicting progression. This work is making some first steps into identifying stable clusters of patients that better represent these underlying subclasses. What is more metric such as Weighted Kappa can be used to give us confidence in these discovered groupings.
Abstract no. 498 Using machine learning methods to create chronic disease case definitions in a primary care electronic medical record
Cord Lethebe, Tolulope Sajobi, Hude Quan, Paul Ronksley, and Tyler Williamson, University of Calgary, Calgary
Introduction The emergence of electronic medical records (EMRs) in primary care in Canada provides a unique opportunity for chronic disease surveillance. Historically, chronic disease surveillance has been difficult in the primary care setting because of the limited administrative data available within primary care. EMR databases include an abundance of rich information that, if properly harnessed, can provide an opportunity for improved chronic disease surveillance. However, the utility of the chronic disease surveillance information is dependent on the quality of the EMR data, as well as the quality of the case identification algorithm that is used. We will present an evaluation of the relative accuracy of a set of case identification algorithms built using machine learning methods as compared to a definition based on clinician expert consultation.
Methods Data was collected from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). CPCSSN is a pan-Canadian organization that collects primary care EMR data for over 1.3 million Canadians and stores it in a standardized format. A chart review study was conducted previously for the presence of 8 chronic conditions (hypertension, osteoarthritis, diabetes, depression, dementia, COPD, Parkinson’s disease, and epilepsy) in a sample of 1920 primary care patients from CPCSSN. The results of this validation study will be used as training data for a series of machine learning classification techniques capable of creating interpretable case definitions. Features will be selected from billing codes, medication prescriptions, specialist referrals, laboratory values, and physician free-text from encounter diagnoses and health-problem lists collected by CPCSSN. Classification and Regression Tree (CaRT) methods, C5.0 decision tree methods, logistic regression using a lasso (or L1) penalty, and forward stepwise logistic regression will be used for feature selection and case definition development. The lasso and forward stepwise logistic regression methods are commonly used for feature selection, but are limited in their interpretability in comparison with decision trees. Complexity parameter values will be determined using k-fold cross validation methods to minimize out-of-bag error. New case definitions will be developed and estimates of sensitivity, specificity, positive predictive value, negative predictive value, and accuracy will be estimated using bootstrap methods. The final case definitions will be compared with committee-created case definitions that are currently implemented in CPCSSN.
Results This is research in progress. Preliminary results show that machine learning methods are capable of creating case definitions that are “as good or better” than committee-created case definitions in terms of their classification accuracy and that the machine learning based definitions involve fewer criteria.
Discussion It appears as though decision tree methods are capable of creating case definitions from a large set of possible features that are easily interpretable.
Conclusion By developing a methodology that can create case definitions in an automated fashion, we can quickly develop and validate case definitions for a variety of chronic conditions and improve surveillance. This is important because quality surveillance allows us to accurately assess the burden of chronic disease in populations and improve efficiency in terms of resource planning and allocation.
Abstract no. 499 Is hearing loss an early complication of diabetes?
Simon Cichosz and Ole Hejlesen, Aalborg University, Aalborg, Denmark
Introduction Hearing loss affects approximately ∼20% of the U.S. adult population and is a major public health worry. Diabetes is a metabolic disease that for many leads to vascular and neurological degeneration. However, evidence on vascular and neurological degeneration effects of diabetes on hearing as an early complication of diabetes is still insufficient.
Methods We examined hearing impairments in undiagnosed and diagnosed adult people with diabetes from the NHANES (2011-2012) and compared them with people without diabetes. Along with the diabetes questionnaire, fasting plasma glucose (FPG) and the oral glucose tolerance test (OGTT) were used to identify people with diabetes. Pure-tone were assessed using a clinical audiometer. Pure-tone average was assessed for low/medium frequencies (500- 2000 Hz) and for high frequencies (3000- 8000 Hz).
Results 2078 people were included in this study. The pure tone average for low / medium frequencies in the worst ear was 15 dB [546] for people with undiagnosed diabetes, 16.7 dB [550.8] for known diabetes and 10 dB [1.731.7] without diabetes. The pure tone average for high frequencies was 28.8 dB [11.274.8] for undiagnosed diabetes, 32.5 dB [12.569.1] for known diabetes and 17.5 dB [3.862.5] without diabetes. The differences remained significant after adjusting for age (P<0.05).
Conclusion Our study suggests that hearing reduction is a frequent complication for people with known diabetes and undiagnosed diabetes. Future work should focus on the mechanisms involved in this diabetes related hearing impairment.
Abstract no. 502 Predicting diabetic foot ulcer outcome: the potential use of on-site observations
Clara Schaarup, Faculty of Medicine, Aalborg University, Aalborg, Denmark
Introduction More than 15% of all diabetic foot ulcers lead to an amputation. Several studies focus on the benefit of using wound outcome prediction. The majority of the model features are based on data from blood tests and various cardiovascular measurements. These features, however, are not available to nurses. This study investigates the potential use of nurses’ on-site observations for wound prediction.
Methods 50 foot ulcer cases were included. Data was obtained from the Danish wound database, Pleje.net, and was collected from January 2006 to October 2016 by Danish community nurses and wound specialists. 19 different wound features were tested. The features covered characteristics related to the three wound stages: the inflammatory phase, the proliferation phase and the maturation phase. We developed a pattern prediction model to forecast individualized development of diabetic foot ulcers into one of two classes: (i) healing, and (ii) no healing, of the diabetic foot ulcer. A positive prediction (test results) corresponds to the prediction (i) healing. In other words, the sensitivity is the number of cases where healing is predicted, in cases where healing actually occurs, divided by the number of cases where healing occurs. Since data includes both nominal and ordinal data types, a binary logistic regression classification was chosen. We used the five features with the highest separability between classes and a 2-folds cross validation.
Results The mean age for the participants was 70 (±14) years, 22% were women, and the overall healing rate among the cases was 72%. The model for predicting healing of diabetic foot ulcers included the following features in the binary logistic regression model: ‘callus’, ‘wound size’, ‘gender’, ‘epithelializing’ and ‘hypergranulation’. The ROC performance of the classifier is seen in Figure 1. The data yielded a cross validated area under the curve (AUC) of 0.66. The threshold can be set arbitrarily. When comparing the performance, one threshold will lead to a sensitivity of 83% and a corresponding specificity of 57%. This corresponds to predicting 8 out of 14 non healing wounds and predicting 30 out of 36 healing wounds. Choosing another threshold, a sensitivity of 63% and a specificity of 84% can be obtained, which corresponds to predicting 12 out of 14 non healing wounds and 23 out of 36 healing wounds.
Discussion The literature has shown several relevant features for predicting wound healing. Most of these features, however, are not for the community nurses. We identified five features from the wound anamnesis which are readily available to the community nurses. Two of these features are used in other prediction models. The remaining three, however, ‘epithelializing’, ‘hyper granulation’ and ‘callus’ have, to our knowledge, not been used in other models.
Conclusion In conclusion, features from community nurses’ wound anamneses are relevant features when predicting wound outcome.
Abstract no. 503 A secure smartphone application for clinical research on wound healing: Nurstrial
Francois-Andre Allaert, Chaire d’évaluation des allégations de santé, ESC et Cenbiotech, DIJON
Intoduction For several decades clinical and observational studies of wounds and wound healing were conducted on paper case report form (CRF) or electronic CRF corresponding to inclusion and follow-up medical visits and between these visits on questionnaires which were rarely daily filled up by the patients and/or the nurses and associated with photos of the wound.
Methods The use of Information Communication Technology and especially Smartphone applications can overhaul this traditional framework of study by introducing closer follow-up of the wound healing process, systematized image capture with few constraints, and even the more active involvement of those must concerned – the patients themselves. The main difficulty to solve is not the development of a Case Record Form on a smartphone being able to manage data, pictures and analogic scale but guaranty the medical secrecy and the protection of medical personal data in accordance to the European directive on data protection.
Results We develop a system of cryptography allowing the involvement of medical practitioner, caregivers and patients themselves in the data records using asymmetric algorithms which have been validate in the framework of a national wound observatory by the French health and data protection authorities. Briefly summarised, the physician downloads the Nurstrial Application and the system proposes to him/her to enter the data for the initial visit, follow-up visits, and undesirable events for the different patients with wounds. For each new patient, the system randomly generates a unique cryptographic identifier using a process leading to the internal creation of a cryptogram encoded on 128 bits generated by UUID-V4 combining hash algorithms and pseudo-random generators. Patients have access to the application via a special-purpose procedure for sequencing information sent in the course of the various transmissions with information sent by the physician, with no risk of collision or duplication, without ever having access to any nominal or identifying information via a process described earlier using a cryptographic identifier encoded on 128 bits generated by UUID-V4 that combines hash algorithms and pseudorandom generators. When information is not sent to the database, a message is sent to the doctor or the patients without knowing their identity.
Discussion The main limitation on the Nurstrial® system is of course the need to have an i-phone or Android type smart-phone but these systems now make up more than 85% of the total number of recent smartphones in France. The Nurstrial® application operates on smartphones with an Android 4.1 operating system and higher and on Apple smartphones with iOS 7 and higher. Here again, few people use old telephones and the phone companies and smartphone manufacturers are the first to urge people to update regularly to new versions.
Conclusion Nurstrial® is a real step forward in terms of quality for the conduct of clinical and observational studies in the area of wound healing. It enables information to be recorded in real-time in the form of text and images that are collected and potentially pooled by all of the actors in the healthcare chain: physicians, nurses, and patients too.
Abstract no. 504 Comorbidities and categorical alcohol intake in relation to upper gastrointestinal (GIB) and intracerebral (ICB)
Shreya Shukla, Adriana Amarilla Vallejo, Stevo Durbaba, Mark Ashworth, and Mariam Molokhia, King’s College London, London
Victoria Cornelius, Imperial College London, London
Introduction Upper gastrointestinal bleeding (UGIB) and intracerebral bleeding (ICB) are associated with serious morbidity and have been linked to medication use, including anticoagulants (AC) and antiplatelets (AP), amongst other risk factors,1 but there are few studies on the risk contribution from co-morbidities and level of alcohol intake.2-4 The aim of the study was to examine effects of comorbidities and alcohol intake with UGIB and ICB in adults ≥18 yrs.
Methods Case-control study of UGIB and ICB in adults using Lambeth DataNet EHRs from 51 Lambeth practices with a total of 286,162 patients, in South East London (2013). Univariable and multivariable logistic regression analyses were carried out using STATA 14, in individuals aged under and over 75 years. Crude analyses were performed adjusted for age and gender. Multivariable analyses were adjusted for age, gender, ethnicity, alcohol intake, QuOF comorbidity codes, smoking status, hypertension, previous cancer, previous stroke, liver disease, renal disease (≥CKD 3), and concomitant drug use known to increase bleeding risk (e.g. SSRIs, COXIBs, NSAIDs and interacting antibiotics). Controls were individuals without any recorded history of UGIB or ICB.
Results Results were stratified by age (<75 and >75yrs) because of significant age interaction, p<0.001. < 75 yrs UGIB significant risks (p<0.05) included: DOAC and PPI use, liver disease, high alcohol consumption (≥28 units/week), SMI and depression, stroke, SSRIs, male gender, asthma, smoking, interacting antibiotics and age for >75yrs PPI use, depression, stroke, male gender and age increased risk of UGIB. We found no association of steroids with asthma and including steroids in our models did not alter bleeding risk estimates.
< 75 yrs ICB significant risks (p<0.05) included: AP, AC and SSRI use, dementia, male gender, African ethnicity, heart failure, hypertension, ≥CKD 3, smoking and age for >75yrs significant risks were AC, AP use, dementia, SSRIs, male gender, heart failure, African ethnicity and ≥CKD 3, with protective effects for alcohol intake <28u/wk.
Discussion Additional to recognised risk factors we identified UGIB risks increased with several comorbidities including SMI, depression and asthma, and alcohol intake ≥28u/week. For ICB risk, main comorbidities included dementia, heart failure, hypertension and ≥CKD 3 with protective effects for alcohol intake <28u/week.
References
Olsen, J.B. et al., 2011. Bleeding risk in ‘real world’ patients with atrial fibrillation: comparison of two established bleeding prediction schemes in a nationwide cohort. Journal of Thrombosis and Haemostasis, 9(8), pp.1460-67.
García Rodríguez, L.A., Lin, K.J., Herna ́ndez-D ́ıaz, S. & Johansson, S., 2011. Risk of Upper Gastrointestinal Bleeding With Low-Dose Acetylsalicylic Acid Alone and in Combination With Clopidogrel and Other Medications. Circulation, 123(10), pp.1108-15.
Hazlewood, K.A., Fugate, S.E. & Harrison, D.L., 2006. Effect of Oral Corticosteroids on Chronic Warfarin Therapy. The Annals of Pharmacotherapy, 40(20), pp.2101-06.
Lin, C.C. et al., 2013. Risk factors of gastrointestinal bleeding in clopidogrel users: a nationwide population-based study. Alimentary Pharmacology and Therapeutics, 38(9), pp.1119-28.
Abstract no. 512 The application of a web-based decision support aid to the selection of treatment options for osteoarthritis
Sally Wortley, Hema Umapathy, and David Hunter, University of Sydney, Sydney
Glenn Salkeld, University of Wollongong, Wollongong
Jack Dowie, London School of Hygiene and Tropical Medicine, London
Introduction Annalisa© (AL) is a web-based decision-support aid grounded in multi-criteria decision analysis (MCDA). Within a single ‘device’ screen, Annalisa synthesizes the best available estimates of the benefits, harms and other factors related to treatment options, captures personal preferences through importance weights for each and combines them into ranked sum score that presents an ‘opinion’ on all treatment options, from most to least preferred. The aim of this study was to evaluate the feasibility and usefulness of the Annalisa decision aid amongst people who are either contemplating their treatment choices for osteoarthritis or in a pre-contemplation phase of decision making.
Methods People were invited to participate in the decision aid study through a stand-alone link on the ‘Myjointpain.org.au’ website and through its Facebook site. The survey consisted of a series of initial questions on which treatment options they were considering and, based on their response, a short OA decision aid. Participants were then given an opportunity to undertake a more thorough detailed personalised decision aid, followed by a set of user evaluation questions.
Results 3072 people clicked to access the decision-aid with 1133 people consenting to being part of the study. 1923 people did not click past the consent section. Out of the 1133 people who consented, 837 people completed the first nine questions (74%), 511 people completed the ‘short version’ Annalisa decision aid (45%). 161 people ‘completed’ the entire decision-aid (14%) and 63 people with full information (6%). Of the 837 who completed the initial questions, 57% chose lifestyle & medicines, 18% medicines and surgery, 16% lifestyle and surgery and 9% medicines only. The most important factors considered by people in their ‘short DA’ were: avoiding serious side effects of treatment, improvement in functioning, avoiding pain, avoiding stiffness, minimizing cost and mid side effects. Of the 511 who completed the DA, 65% were either not surprised or only slightly surprised by the highest ranked option generated as a result of their own importance weights for the benefits, harms and other factors. 32% of people said that the DA changed their views on the best treatment and all said that they were very likely to see their GP or specialist as a result in the next 6 months. Of the 161 people who completed the more thorough and personalized DA, the highest ranked treatment options were: strength training, followed by cardiovascular exercise, education and paracetamol. Of the small number of people who answered the evaluation questions, 59% said they would rate their experience in using the Annalisa decision aid as positive and 42% said that their experience of using the decision aid would help them improve the quality of their future decisions.
Discussion & Conclusion Quantitative decision aids are useful for some people and not others. Given the drop off in participation from the short version DA to the longer ‘personalised’ DA, the length and display of decision aid needs to be tested with those with osteoarthritis. People are interested in the effectiveness of lifestyle interventions.
Abstract no. 513 The ELAStiC (electronic longitudinal alcohol study in communities) project
Ashley Akbari, Ronan Lyons, Damon Berridge, FARR Institute (CIPHER - Swansea), Swansea
John Gallacher, Oxford University, Oxford
John Macleod, Jon Heron, Mathew Hickman, Liam Mahedy, University of Bristol, Bristol
Mark Bellis, Public Health Wales, Cardiff
David Fone, Shantini Paranjothy, Daniel Farewell, Lazlo Trefan, Annette Evans, Frank Dunstan, Vanessa Gross, and Simon Moore, Cardiff University, Cardiff
Karen Tingay, Administrative Data Research Centre - Wales, Swansea University, Swansea
Amrita Bandyopadhyay, Swansea University, Swansea
Introduction The ELAStiC (Electronic Longitudinal Alcohol Study in Communities) project was established to determine factors that predict pathways into alcohol misuse and the life-course effects of alcohol use and misuse on health and well-being. This is achieved through accessing existing longitudinal data that are key sources of evidence for social and health policy, developing statistical methods and modelling techniques from a diverse range of disciplines, working with stakeholders in both policy, practice and the third sector to bring relevance to the work, and to bring together a diverse team of experts to collaborate and facilitate learning across diverse fields.
Method The project is linking data that include cohort studies such as UK Biobank, ALSPAC (Avon Longitudinal Study of Parents and Children), Millennium Cohort Study, British Household Panel Survey, Understanding Society, E_CATALyST (Caerphilly Health and Social Needs Electronic Cohort Study) and WECC (Wales Electronic Cohort for Children). These data will be linked with routine data from primary and secondary healthcare in England, Scotland and Wales. Additional data from education and police data source will also be linked as part of the project. The main work packages for the project are:
Methodological Innovations Methodological developments in mechanisms for correcting bias in reporting alcohol consumption and for combining routine data with cohort data the application of Markov models for examining the extent to which past behaviour influences future behaviour, and econometric hedonic pricing methods for providing insights into the costs of alcohol-related harm.
Pathways into Harm Do family structure, household composition, youngsters’ previous ill-health and educational attainment predict their use of alcohol and what socio-economic factors and household transitions contribute to hazardous alcohol consumption in adults?
Secondary Harms What is the effect on children’s health and educational achievement of living in households in which one or more adults has experienced alcohol-related harm?
Mental Health & Well-Being What is the relationship between alcohol consumption, hospital admission and mental health in adults and children?
Results The results of the data linkage between the multiple cohorts and health, education and police data will be reported. The challenges of linking cohort and other data types from different nations will be discussed.
Discussion The issues surrounding UK wide data linkage and access are likely well known, especially involving numerous cohorts and countries. Our project has looked to deal with these limitations and delays by piloting methodologies within countries and cohorts which we have had more success in linking and obtaining, before expanding once the data becomes available.
Conclusion Our project will aim to provide evidence that informs the UK Government’s commitment to “radically reshape the approach to alcohol and reduce the number of people drinking to excess”, by working with existing longitudinal data collected in the UK to inform policy and practice.
Abstract no. 514 Factors affecting patients’ use of electronic personal health records
Alaa Abd-alrazaq and Hamish Fraser, Yorkshire Centre for Health Informatics, University of Leeds, Leeds
Peter Gardner, School of Psychology, University of Leeds, Leeds
Introduction Electronic personal health records (ePHRs) are web-based tools that enable patients to access parts of their health records and perform other services such as booking appointments and requesting repeat medications. Despite many potential benefits of ePHRs, the adoption rate of these tools is often very low. We describe here a systematic review of the literature regarding factors that affect patients’ use of ePHRs.
Methods This systematic review employed five search sources: 44 bibliographic databases, hand searching, checking reference lists, contacting experts and professionals, and web searching. Further, three groups of search terms, related to population, intervention and outcome, were used for searching databases. Detailed study eligibility criteria and forms were developed in this review. The Mixed Methods Appraisal Tool (MMAT) was used to appraise the quality of the included studies. The results of studies were synthesised narratively according to the outcome: intention to use, subjective measures of use, and objective measures of use.
Results 4843 titles and abstracts were screened, 245 full texts were read, and 85 publications were included in the review. According to the MMAT, the quality of qualitative studies was clearly higher than quantitative and mixed-methods studies. Among 20 studies that assessed the factors affecting patients’ intention to use ePHRs, 57 factors were grouped into 4 main categories: personal factors (35 factors), human-technology interaction factors (11), organisational factors (10), and social factors (1). The factors that affect the subjective use of ePHRs were tested in 17 studies classified into 4 groups: 32 personal factors, 8 human-technology interaction factors, 3 organisational factors, 1 social factor. The factors that influence the objective use of ePHRs were assessed in 46 studies classified into 3 groups: 70 personal factors, 11 human-technology interaction factors, and 14 organisational factors. The most tested factors were age (69 studies), gender (60), educational level (34), race (29), perceived usefulness (26), and income (25).
Discussion This review found more than 100 exclusive factors that may affect the acceptance of ePHRs. However, most of these factors either were examined by very few studies, or there is no consensus on their effect among the included studies. Therefore, definitive conclusions regarding the effect of these factors on intention to use, subjective use, or objective use of ePHRs could be drawn for only 24 factors. Of these 24 factors, only 4 factors are common among the 3 outcomes (intention to use, subjective use, and objective use): internet access (positive), perceived usefulness (positive), privacy concerns (negative), and gender (no relationship). The review is more rigorous than similar reviews in terms of several aspects such as eligibility criteria, search sources and terms, study quality assessment, and findings synthesis.
Conclusion The factors that may affect patients’ acceptance of ePHRs that have been studied are numerous and varied. In order to improve the adoption rate, the factors supported by multiple studies should be considered carefully before and after the implementation of ePHRs. Future researchers should conduct more theory-based and longitudinal studies that assess objectively the factors affecting the use of ePHRs.
Abstract no. 515 The European injury database: supporting injury research and policy across europe
Samantha Turner, Farr Institute, Swansea University, Swansea
Ronan Lyons, Farr Institute (CIPHER - Swansea), Swansea
Wim Rogmans, Eurosafe, Amsterdam
Rupert Kisser, Eurosafe, Vienna
Bjarne Laursen, National Institute of Public Health, Copenhagen
Huib Valkenberg, Consumer Safety Institute, Amsterdam
Dritan Bejko, Luxembourg Institute of Health, Luxembourg
Robert Bauer and Monica Steiner, Austrian Road Safety Board, Vienna
Gabriele Ellsaesser, State Office of Environment, Health and Consumer Protection, Brandenburg
Ashley Akbari, Swansea University, Swansea
Introduction Although various injury data sources exist in Europe many lack sufficient size, scope, detail or comparability, to support injury prevention research or policy development. Emergency department (ED) records offer one of the most comprehensive sources of injury data however, heterogeneous hospital data collection systems prevent comparative analyses between countries.
Methods As part of the Joint Action on Monitoring Injuries in Europe (JAMIE) project, and now the BRIDGE-Health (BRidging Information and Data Generation for Evidence-based Health Policy and Research) development the European Commission (EC) funded the development of a standardised European Injury Data Base (IDB). The IDB comprises two datasets: the Full DataSet (FDS) and Minimum DataSet (MDS). Although the MDS collects less detail than the FDS it is simpler for countries to adopt, and still sufficient to allow enumeration of injuries in key areas such as the home, leisure, work, road, falls, sports, and self-harm. Training, guides and rigorous quality checks, ensure consistency across participating countries.
Results To date, 26 countries have submitted 7,170,069 ED records (years 2009-2014) to the IDB in MDS format, and 20 countries have provided reference population data, enabling the calculation of ED attendance rates by country. As an exemplar, in 2013, ED rates for all injuries varied between 116 per 1000 population in Luxembourg to 33 per 1000 population in Finland. The MDS has provided a valuable source of data for several organisations across Europe, and can be accessed via several channels, including an online tool. The MDS strives to contribute data to the “European Core Health Indicators” (ECHI), including the “home, leisure and school accidents” indicator (ECHI29b).
Discussion The range in national IDB rates (e.g. 33 – 116 injury attendances per 1000 population) is quite large, suggesting that injury morbidity isn’t the only influencing factor. Variations in national health care systems, accessibility and utilisation of EDs, differences in data sampling methods and sample sizes, and other data quality issues, are likely to affect IDB estimates. Nonetheless, the IDB reports one of the lowest ranges in rates when compared to other European level health data systems.
Conclusions The development of the MDS is a great achievement and provides Europe with a valuable source of comparable injury data. Work is ongoing to ensure the IDB-MDS is as valid and representative as possible.
Abstract no. 517 Challenges in using hierarchical clustering to identify asthma subtypes: choosing the variables and variable transformation
Matea Deliu, University of Manchester, Health e-Research Centre, Manchester
Tolga Yavuz, University of Manchester, Manchester
Matthew Sperrin and Umit Sahiner, Imperial College London, London
Danielle Belgrave Cansin Sackesen, Adnan Custovic and Omer Kalayci, Haceteppe University, Ankara
Introduction The use of unsupervised clustering has identified different subtypes of asthma. Choosing the variables to input into the clustering algorithm is one of the important considerations. The majority of previous studies selected variables based on expert advice, whilst others used dimension reduction techniques such as principal component analysis (PCA). We aimed to compare the results of unsupervised clustering when using raw variables, or variables transformed using dimensionality reduction techniques.
Methods We recruited 613 asthmatics aged 6–23 years (Ankara, Turkey). We conducted extensive phenotyping, with 49 variables including demographic data, sensitization, lung function, medication, peripheral eosinophilia, and markers of asthma severity. We performed hierarchical clustering (HC) using: (1) all variables and (2) PCA-transformed variables.
Results PCA revealed 5 components describing atopy and variations in asthma severity, which were then used to infer cluster assignment. The optimal HC solution in both PCA-transformed and raw untransformed data identified 5-clusters which were not identical. Both identified mild asthma with good lung function, severe atopic asthma and late-onset atopic mild asthma. Clustering without PCA identified early-onset severe atopic asthma and late-onset atopic asthma with high BMI, whilst early onset non-atopic mild asthma in females was identified in HC with PCA. However, cluster stability was poor. A comparison of the two cluster outputs revealed four key features driving cluster allocations: age of onset, asthma severity, atopic status, and asthma exacerbations. Re-clustering with these features markedly improved cluster stability.
Conclusion Different methodologies applied to the same dataset identified differing clusters of asthma. We identified four main features that could be represent a new framework for clustering children with asthma. This will eventually bring us one step closer to identifying heterogeneity and subtypes of asthma thereby paving the way towards precision based medicine.
Abstract no. 521 Main usability problems of a home monitoring tool for heart failure patients and COPD patients: connecting medical hardware with app interface
Hester Albers, Martine Josefien Maria Breteler and Monique Jaspers, Academic Medical Center, University of Amsterdam, Amsterdam
Gaby Anne Wildenbos and Linda Peute, FocusCura Futurelab for Care, Driebergen Rijsenburg
Introduction Home telemonitoring (HTM) for the chronically ill is increasingly used as a solution to the growing problem of an aging population. Although it is suspected that older users experience difficulties with the use of monitoring systems and the associated measurement equipment, little evidence-based information on the concrete issues older adult users experience exists. Therefore, it is important that usability evaluations are performed on existing telemonitoring systems. This study focuses on the evaluation of cVitals, a HTM application. The aim of this study is to evaluate which usability issues older Chronic Obstructive Pulmonary Disease (COPD) and Heart Failure (HF) patients encounter using cVitals at home.
Method A usability evaluation combining think aloud (TA) sessions and an interview with the designer of cVitals was conducted. The interview with the designer was structured according to the extended usability guidelines of the Healthcare Information and Management Systems Society (HIMSS). The TA study was carried out with 5 HF and 5 COPD patients at their home. Observed problems and suggestions were analysed and categorized using the extended HIMSS guidelines and the framework for mHealth for older users.
Results The designer of cVitals showed that 13 of the 15 aspects of the extended HIMSS guidelines were implemented in cVitals. The usability evaluation identified 51 usability issues and 14 suggestions for improvement. Of the identified usability problems by the TA, 8 problems were related to the connection between the measurement equipment and the home monitoring application. Connection issues were mostly caused by a limited reach- and problems with the Bluetooth connection. The most used classifications were ‘no knowledge of functionalities’ and ‘effective information presentation’.
Discussion The TA study was an effective method for identifying usability problems with cVitals. It identified several different usability problems, especially regarding the connection between hardware and the home monitoring application. Patients reported high satisfaction with the system. Identified usability issues corresponded with other usability studies, carried out with older HF and COPD patients.
Conclusion The usability evaluation helped to define the usability problems and suggestions for improvement were described. This can lead the further development of this and other home monitoring application(s) and contributes to evidence-based knowledge on usability of HTM applications for older users. Better usable HTM applications leads to a higher acceptance of HTM by this target group.
Abstract no. 525 High-dimensional statistical approaches for heterogeneous molecular data in cancer medicine
Nicolas Staedler, Roche, Basel
Frank Dondelinger, Lancaster University, Lancaster
Sach Mukherjee, German Centre for Neurodegenerative Diseases, Bonn
Introduction Molecular interplay plays a central role in basic and disease biology. Patterns of interplay are thought to differ between biological contexts, such as cell type, tissue type, or disease state. Many high-throughput studies now span multiple such contexts and the data may therefore be heterogeneous with respect to patterns of interplay. This motivates a need for statistical approaches that can cope with molecular data that are heterogeneous in a multivariate sense.
Methods In this work, we exploit recent advances in high-dimensional statistics1-2 to put forward tools for analysing heterogeneous molecular data. We model the data using Gaussian graphical models,3 and develop two useful techniques based on estimation of partial correlations using the graphical lasso:4 a two-sample test that captures differences in molecular interplay or networks, and a mixture model clustering approach that simultaneously learns cluster assignments and multivariate network models that are cluster-specific.
Results We demonstrate the characteristics of our methods using an in-depth simulation study, and proceed to apply them to proteomic data from The Cancer Genome Atlas (TCGA) pan-cancer study,5 consisting of protein expression measurements for 181 cancer signalling proteins in 3,500 patients spanning 11 different cancer types. We first test for pairwise network differences between cancer types. Subsequently, we use the mixture model to identify clusters of patients that present similar protein signalling networks and we visualize the networks.
Discussion Our analysis of the TCGA data provides formal statistical evidence that protein networks dier significantly by cancer type. Furthermore, we show how multivariate models can be used to refine cancer subtypes and learn associated networks.
Conclusion Our results demonstrate the challenges involved in truly multivariate analysis of heterogeneous molecular data and the substantive gains that high-dimensional methods can offer in this setting.
References
Stadler, N. and Mukherjee, S. (2013a). Penalized estimation in high-dimensional hidden Markov models with state-specific graphical models. Annals of Applied Statistics, 7:2157-2179.
Stadler, N. and Mukherjee, S. (2013b). Two-sample testing in high-dimensional models. arXiv.org:1210.4584.
Friedman, J., Hastie, T., and Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical Lasso. Biostatistics, 9(3):432-441.
Rue, H. and Held, L. (2005). Gaussian Markov random fields: theory and applications. CRC Press, London.
Akbani, R., Ng, P. K. S., Werner, H. M., Shahmoradgoli, M., Zhang, F., Ju, Z., Liu, W., Yang, J.-Y., Yoshihara, K., Li, J., et al. (2014). A pan-cancer proteomic perspective on The Cancer Genome Atlas. Nature Communications, 5(3887).
Abstract no. 529 Survival following discharge from critical care
Angharad Walters, Ronan Lyons and Damon Berridge, FARR Institute (CIPHER - Swansea), Swansea
Richard Pugh, Betsi Cadwaladr University Health Board, Rhyl
Ceri Battle and David Hope, Abertawe Bro Morgannwg University Health Board, Swansea
David Rawlinson, Emergency Medical Retrieval & Transfer Service (EMRTS) Cymru, Swansea
Tamas Szakmany, Aneurin Bevan University Health Board, Newport
Introduction Critical care provides specialist treatment for patients with life-threatening injuries and illnesses. Outcomes data are essential to identify where resources need to be focused to provide safer care. Limited data exists on mortality risk factors for ICU survivors in Wales, in particular, little is known about the organisational factors affecting 1-year outcome. The objective of the study is to determine survival following discharge from critical care and to identify the factors that increase the risk of mortality.
Methods Anonymised critical care data reported from 1st April 2006 are held within the Secure Anonymised Information Linkage (SAIL) databank. The critical care data was linked with ONS mortality data, the Patient Episode Database for Wales (PEDW) inpatients data (to obtain hospital admission details) and the Welsh Demographic Service dataset (to obtain demographic details). Details of patient care during the critical care admission such as the organs supported and discharge details such as the time of day, along with patient demographics such as age, sex and socio economic factors were used in the analysis. The cohort included the first critical care episode for Welsh patients, aged 16+, who were discharged alive between 2006 and 2013. Patients were followed up to 365 days after discharge, outward migration or death.
Results 40,631 patients discharged alive from critical care were followed up. The estimate of the risk of death at days 30, 90 and 365 following discharge is 7.4%, 11.5% and 19.5% respectively. Factors from Cox regression that increase the risk of mortality include male sex, increasing age, increasing comorbidity score, increasing length of stay, unplanned admissions, being discharged early due to critical care bed shortage (baseline – ready to be discharged) and discharged in the evening in hours and out of hours (baseline – afternoon in hours).
Conclusion Determining the factors associated with mortality allows patients at highest risk of death to be identified. Based on this analysis, evening discharges from critical care should be avoided.
Abstract no. 530 The exploratory research of brain stem blood flow in traumatic brain injury: a challenge for clinical data science
Gleb Danilov, Natalya Zakharova, Alexander Potapov, Igor Pronin, Eugenia Alexandrova, and Andrey Oshorov, Burdenko Neurosurgery Institute, Moscow
Introduction Since the introduction of perfusion computerized tomography (PCT) into clinical practice, it was rarely used for brain stem blood flow evaluation in traumatic brain injury (TBI). The aim of this study was to assess a complex relationship between brain stem blood flow and pathophysiological/clinical manifestations of TBI using data scientist’s toolbox.
Methods To explore the patterns of brain stem blood flow (BBF) in TBI under diverse pathophysiological conditions disparate data from electronic health records, CT-scanner, monitoring devices and other sources were acquired, structured, linked and analysed. Brain stem perfusion was measured at the level of midbrain in the standardized regions of interest. In a period between 2005 and 2014 we collected data on 81 patients with acute TBI, which reflected the structural and functional changes in brain, its blood flow and clinical manifestations, as well as the state of cerebral perfusion regulation. We used exploratory, cluster and time series analysis with pattern recognition techniques. All the data processing and statistical calculations were made with the R programming language and environment (www.r-project.org).
Results The majority of studied patients were severely injured (81.5%) in road-traffic accidents (65%). The average values of BBF were 27-29 ml/100g/min, ranging significantly and increasingly in severely injured patients. The average level of brain stem blood flow was lower compared to hemispheric (26-28 ml/100 g/min, p < 0.05). The lowest BBF values were found inside the haemorrhagic lesions (4.0 ml/100 g/min). Oppositely, we explored that the high BBF (up to 76 ml/100 g/min) was related to cerebral circulation autoregulation failure (p <0.05), which, in turn, was accompanied by intracranial hypertension. Multivariate time series classification was used to distinguish the patterns of intracranial pressure (ICP) leading to autoregulation disturbance. The key factor for disregulation was ICP > 15 mmHg which appeared to be lower than the accepted ICP threshold of 25 mmHg for surgical interventions.
Discussion The study combined a prospective data collection, ad hoc analysis, observational component. The exploratory data analysis followed by conventional hypothesis testing enabled to explain the lowest BBF values by brain stem damage and the extremely high BBF levels by autoregulation failure. The time series classification was implemented for specific ICP patterns recognition. In fact, the linear relation between neurological, magnetic resonance, pathophysiological signs and BBF was rarely observed in our study. One and the same blood flow level was accompanied by diverse neurological and pathophysiological manifestations, and vice versa. We found that brainstem blood flow parameters may deteriorate much earlier than the signs of axial dislocation and secondary brain stem damage appear. That’s why that parameters could be considered as additional criterion to support the decision of decompressive surgery.
Conclusion Data science offers unrivalled opportunities for clinical research changing the future of evidence based medicine. In addition to randomized controlled trials data science enables sophisticated basic research of brain pathophysiology. In this study a diverse and clinically unpredictable brain stem blood flow was explored and simply explained by basic contributors using a complex data analysis.
Abstract no. 532 Design of a BPM model for Crohn´s clinical process
Alberto Antonio De Ramon Fernandez, University of Alicante, Murcia
Ruiz-Fernández, Virgilio Gilart, and Diego Marcos Jorquera, University of Alicante, San Vicente del Raspeig
Introduction Crohn´s disease belongs to the intestinal inflammatory diseases. It causes the inflammation of the intestinal tract and an important loss of quality of life for the patients. Its treatment generates a high cost for the national health care systems. This article pursues the integration of a Business Process Management (BPM) model to overcome the weaknesses detected in the current clinical process.
Methods A wide range of national and international clinical guidelines for the Crohn’s disease has been analysed to standardise them and detect weaknesses in the current process. This study was contrasted with staff of the area of health sciences of the University of Alicante. This work focuses on the implementation of improvements by BPM. Its agile and flexible use allows the process to be adapted to unexpected changes. The development proposed allows us to obtain a global model that integrates all processes involved in the treatment of the disease. It also includes the phase of psychological support to the patient.
Results As a result, a model based on the standard BPMN is proposed. Moreover, the main weaknesses of the current process are detected: 1) Lack of standardised clinical guidelines 2) Faecal calprotectin test carries out only in the final step of the diagnostic phase 3) Lack of psychological support during the process 4) Large number of visits to the primary care centres 5) Lack of clear and updated information throughout the clinical process 6) High cost associated with the process 7) Large number of compatible symptoms with other diseases 8) Weak implementation of IT´s in clinical processes 9) There are no clinical Decision Support System (DSS) 10) Lack of empowerment of patient.
Discussion The novelty of this work is the design of a standardised model for the clinical process of the Crohn´s disease applying BPM. It allows us to manage more efficiently the process. So far, BPM is mainly used to improve health administrative processes.
Conclusion BPM applied to clinical processes involves a new management paradigm in managing chronic diseases, providing efficiency and patient satisfaction. Thus, a dynamic model adapted to the needs of the patient is achieved. It improves the empowerment of the patient and overcomes the deficiencies in the current clinical process.
Abstract no. 534 Towards a medical informatics curricula visualization and analysis tool
Tom Broens, Floris Wiesman and Monique Jaspers, Academic Medical Center / University of Amsterdam, Amsterdam
Introduction Educational programs (curricula) are subject to change. Quality control, new didactical methods and upcoming research fields stimulate educators to change the content of their curricula and their education methods on a regular basis. This is also the case for the medical informatics bachelor and master programs at the Academic Medical Center Amsterdam, currently serving around 250 students and 100 involved lecturers. To execute a comprehensive program, all stakeholders require a clear view of the programs’ exit qualifications, the courses’ learning outcomes, the position and relation of courses to the program, and links between the courses in terms of topics covered. Without tooling, grasping a program’s contents and keeping it consistent, considering its evolution, is challenging. This abstract discusses ongoing work on the development of an ICT tool to visualize and analyze curricula to facilitate their consistent management.
Methods A literature study was performed to identify similar tools and develop an information model that provides the foundation for the tool. A prototype was developed and filled with information of the two Amsterdam programs. The prototype was used to gain insight in user experiences and formulate future improvements.
Results All information and visualization presented in the tool is generated from structured data in the underlying curricula database. The tool provides an overview of the courses, credit size, learning tracks, learning goals and references to IMIA topics. This way of handling information enables different views on a curriculum that can also be used for analytics. For example, a view of a learning track, that has different activities in different courses, can be shown comprehensively and chronologically. It indicates where different activities take place and what learning objectives are served. This enables educators to verify and maintain this track. Another example, analytics can be performed on the recorded references to IMIA topics. A heatmap is created to reveal focus areas in a program. A final example, the tool can visualize the relation between program’s exit qualifications and individual course objectives, including if required skill levels are met.
Discussion The usage of the tool stimulated discussion amongst educators. For example, different overlapping topics where identified and a more consistent program could be realized. Also terminology proved to be ambiguously interpreted amongst educators in the program. The tool enabled the identification of these differences in understanding and foster discussions using real examples. Additionally, the tool provided analytics to uncover deeper characteristics of a program. A future version of the tool will allow educators to change and add information by themselves. This makes curricula design a shared responsibility. The tool will stimulate collaboration between educators who can be supported in developing courses in a structured way using pre-defined protocols and auditing processes. Also secondary education information will be (partially) generated, for example assessment plans, and completed by users with less overhead compared to a non-automated process.
Conclusion The curricula visualization and analysis tool provides a way to consistently monitor and maintain an education program. It stimulates shared responsibility, discussion and provides opportunity for lessening administrative burden.
Abstract no. 539 XML representation of WHO international classification of health interventions (ICHI)
Sukil Kim, Catholic University of Korea, Seoul
Jae Jin Lee, WHO Collaborating Center for International Classifications and Terminology, Seoul
Jean Marie Rodrigues, The Catholic University of Korea, Seoul
Beatrice Trombert, Université Jean Monnet Saint-Etienne, Paris
Huib Ten Napel, INSERM U1142 LIMICS, Paris
Since 2006, the WHO-FIC network has been developing an International Classification for Health Intervention (ICHI) based on an ontology framework defined in ENISO 1828 named Categorical Structure, with 3 axes and 7 characters. It was planned that ICHI should encompass the granularity of the ICD 9CM Volume 3. After several tests with the ICHI alpha 2 (May 2016) version, we analysed from bottom up 574 ICHI alpha 2 codes by modelling them in XML, and show that the existing coding structure does not allow a semantic representation necessary to ensure interoperability with other existing coding system for medical and surgical interventions. We have thus developed a more refined version of ICHI using XML model: using as the root element, which is a set of elements in the XML schema, we have included 3 attributes, code, interventionType, and title, to each element. Within an element there are 7 further elements: title, comment, linkedClassification, content, composition, inclusion, and exclusion. The element is composed of three axes (Target, Action and Means), and an axes can hold more than one object type. The element allows distinction of procedures that contain multiple interventions. Our XML model of ICHI will be able to cover the problems of granularity of the previous model.
Abstract no. 542 Real time capture of routine clinical data in the hospital electronic health record using a purpose built power-form
Neil Bodagh, Queen Mary University, London
Andrew Archbold and Roshan Weerackody, Bart’s Heart Centre, London
Mike Barnes, William Harvey Research Institute, Queen Mary University, London
John Robson, Clinical Excellence Group, Queen Mary University, London
Adam Timmis, Farr Institute of Health Informatics Research and Bart’s Heart Centre, London
Introduction The electronic health record (EHR) is the major repository of clinical data in the NHS. It is a huge potential resource but remains severely under-utilised to the point that very little of the UK’s research output is based on routinely collected clinical data. The reasons are complex but ultimately reflect the fact that these data are rarely entered into the hospital EHR in a form that allows for their organized storage and digital download.
Methods We have developed a SNOMED-based electronic power-form comprising a user-friendly interface for real-time entry of clinical data into the EHR during cardiac outpatient consultation. Our aim was to capture outpatient clinical data in a form that allows for automatic development of summary patient reports and for batch download of de-identified data for audit and research.
Results During the first 4 months after installation of the power-form, consultant utilisation averaged 60% for the 327 new patients seen during that period. Presenting symptoms, examination findings, investigations, diagnosis, initial treatment and disposal (>120 fields) were entered in real time during consultation and a structured summary report was developed. This was made available for electronic transfer directly into the patient’s EMIS file in the primary care record, permitting same-day delivery of the report and obviating the need for a dictated clinic letter. Batched download of the digital data was successful, with sample analytic findings as follows:
Patient ethnicity: S Asian 44%, white 34%, black 15%
Presenting symptom: chest pain 41%, dyspnoea 11%, palpitations 10%, dizzy attacks/syncope 8%, hypertension 7%
Diagnosis: non-cardiac chest pain 24%, angina 11%, coronary disease 7%
Disposal: discharged to GP 74%, follow-up appointment 11%, cath lab waiting list 6%, referral to specialist clinic 9%
A total of 58 GPs and 37 patients have been surveyed on the utility of the report. Satisfaction has been reported with same day delivery of the summary report, its layout and the adequacy of the information provided for patients’ understanding of their condition and GPs’ clinical needs. Using a 5 point Likert scale (1=much less useful – 5=much more useful) both GPs (average Likert score 4.32) and patients (average Likert score 4.62) find the outpatient report to be more useful than the conventional dictated clinic letter.
Conclusion This is the first report of power-form development for entry of routinely collected cardiac outpatient data into the hospital EHR. The data are stored in a form that permits: (1) automatic generation of a summary report for same-day delivery into the primary care record and (2) batch download of de-identified digital data for audit. Integration of the system with programmes of generic patient consent will open-up the hospital EHR to real-world clinical research.
Abstract no. 543 Regional administrative health databases in italy: a census and practical remarks
Rosaria Gesuita and Edlira Skrami, Marche Polytechnic University, Ancona
Vincenzo Guardabasso, University Hospital ‘Policlinico Vittorio Emanuele’, Catania
Simona Villani and Paola Borrelli, University of Pavia, Pavia
Antonella Zambon, University of Milano-Bicocca, Milan
Paolo Trerotoli, University of Bari ‘Aldo Moro’, Bari
Working Group ‘Observational Studies’, SISMEC - Italian Society for Medical Statistics and Clinical Epidemiology, Pavia
Introduction Administrative Health Databases (AHD) have been widely used in Italy, some dating back two decades or more. Epidemiological observations from AHD data can be useful to stakeholders to support health policies and services. AHD scope and availability for epidemiological studies in Italy are not well known or documented. A Research project from the SISMEC Working Group on Observational Studies was funded by the Italian Ministry of Health and the Puglia Region, to perform a census of Electronic ADH in Italy (‘Electronic health databases as a source of reliable information for effective health policy”, Project RF-2010-2315604). The project aimed at evaluating methodological issues related to the use of AHD for epidemiology, and focused on the public regional health administrations.
Methods A census was completed in 2016 after sending questionnaires to the various regional administration contact persons for AHD, that receive mandatory data from hospitals and local health units. In 2 out of 21 information on AHD was directly gathered from institutional web sites. Several features were collected for AHD, including type, time span, population coverage, missing data and quality, IT system, unique linkage key and anonymization. A web site was created to make this information publicly available (http://www.sismec.info/arches).
Results The survey found 349 AHD, pertaining to 29 types, from 21 Regional Health Administrations. The results documented for the first time their detailed features, and specifically those concerning linkage keys and privacy protection. The number of AHD per region varied between 6 and 39 the most represented types were home and residential care data over 65% of AHD report protection of anonymity. Linkage key were available in 67% of AHD, and were based on local regional procedures. The survey confirmed that AHD in Italy are fragmented at the regional level. The different regional jurisdictions of local government manage the regional data on independent IT systems, because of implementation of IT after the approval of Constitutional laws in 2001 devolving health legislation to regional governments, resulting in a fragmented national context. Although many of those AHD are then merged by the Ministry of Health, the opportunities for nation-wide observational studies on secondary administrative health data collected in AHD are unclear, and any independent study proposal would run into several barriers, due to privacy regulations, confusing process of approval, and heterogeneity of AHD. At present any data-linkage procedure across regions incurs in the barrier of different pseudo-anonymous identification codes being used in different regions.
Conclusions Several problems affect the feasibility of nation-wide observational studies on secondary data from the wealth of AHD in Italy. This is especially true for epidemiology researchers, interested in research rather than in organisational analyses. However, independent research can provide the Italian Health System with new, fresh insights that could expand the borders of health systems routine monitoring. Problems of privacy protection, heterogeneity and fragmentation could be addressed at a national level, taking advantage of experience from other countries. Presently in Italy patients flow freely across health services, information about their care does not.
Abstract no. 554 Establishing safe and efficient “read-through” indexes for Scottish informatics and linkage collaboration
David Clark and Gerald Donnelly, National Records of Scotland, Edinburgh
Albert King, Scottish Government Education Analytical Services, Edinburgh
Introduction National Records of Scotland (NRS) provide the Trusted Third Party (TTP) Indexing Service on behalf of the Scottish Informatics and Linkage Collaboration, encompassing data linkage projects supported by Farr Institute Scotland, Administrative Data Research Centre – Scotland, Urban Big Data Centre and the Scottish Government. The role of NRS is to match the personal identifiers submitted by data controllers to the national research spine and generate study and datasetspecific index numbers. These indexes are used to link pseudo-anonymised records accessed by approved researchers in a safe haven. To avoid both retaining linked research datasets, and repeatedly sharing personal identifying information from datasets required in multiple projects, NRS are developing a series of “read-through” index keys, which can be re-used and facilitate safer and more efficient data linkage.
Methods NRS are in the process of agreeing a series of memorandums of understanding (MoU) with various Data Controllers of administrative datasets in order to establish safer and more efficient linkages of datasets. Under each MoU, the data controller asks NRS to process their dataset by linking it to the national research spine and creating anonymised “read-through” index keys which the Data Controller will hold at the person-level on their own dataset. NRS will maintain a look-up of the “readthrough” against the spine. This means for approved research studies involving their already indexed data, the Data Controller just needs to send the read-through keys (without any other personal identifying information) for the people comprising the study cohort, to the indexing team to generate study-specific keys in the usual manner. It can also allow the data provider to receive the “read-through” keys and study-specific index numbers from the TTP, when the research cohort originates from a dataset held by a different data controller.
Results So far MoU’s have been agreed with NHS Scotland for national health data , for primary care data from consenting General Practices, Scottish Government Education Analytical Services for school pupil census and Communities Analysis for housing data, University of Edinburgh for Scottish Mental Surveys, and NRS for Census and Vital Events data.
Discussion We anticipate that the creation of read-through indexes will deliver the following benefits without the need to increase personal data held by the TTP Indexing Service or Data Controllers, and in a way which preserves Data Controllers direct control over the use of the data they hold:
Reduced privacy risks as individual identifiers required for indexing would be shared once rather than on a project-by-project basis
Increased use of administrative data for research with benefits to public policy and academic research outputs that inform practice in health, education, and other fields
Reduced burden on data controllers as identifiers would need to be extracted only once
Improved efficiency of linkage as the indexing team would need to carry out indexing only once
Conclusion Utilising read-through keys considerably cuts down the amount of personal data which regularly have to be transferred to NRS, and then matched using probabilistic methods to the spine on a project-by-project basis.
Abstract no. 557 A intelligence application of health information monitoring and telehealthcare for surgical operations on elderly patients
Jin-Ming Wu and Te-Wei Ho, Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei
Yao-Ting Chang, Chung-Chieh Hsu, and Feipei Lai, Department of Computer Science and Information Engineering, National Taiwan University, Taipei
Introduction To provide the safety and high quality health care and to provide the telehealthcare for elderly people by information and communication technology, we proposed a knowledge-based telehealthcare smartphone application (APP) with the artificial intelligence mechanism in intelligent disease management. The aim of this APP was to enhance the early recovery of elderly patients who received surgery.
Methods This study investigated a smartphone application developed to serve the functions of drainage follow-up, nutritional monitoring, symptom management, activity management, and wound care. To provide real-time remote care, we also designed a platform for the patients and medical staff to permit reviews of the records.
Results We have implemented a smartphone application for both Android and iOS versions. The APP could automatic provide a summary of the patient’s health status based on the measurement records. According to the previous preliminary results, twenty patients at the National Taiwan University Hospital received perioperative care via this APP as the telehealth group. During the study period, we retrospectively collected an additional 20 demographically matched cases as a control group. The telehealth group had a lower body weight loss percentage relative to the control group during a 6-month follow-up period (4.8 ± 1.2% vs. 8.7 ± 2.4% p <0.01).
Discussion Although the telehealth group had a lower body weight loss percentage, they had more outpatient clinic visits than did those in the control group (9.8 ± 0.9 vs. 5.6 ± 0.8 p <0.01). In the future, we will conduct a further study for the clinical effectiveness and cost-effectiveness of elderly patients who received surgery.
Conclusion This study supported the feasibility of a smartphone application for the perioperative care of patients to promote a lower body weight loss and the collection of comprehensive surgical records. With the advanced functions of this APP, we expect to acquire further clinical evidence to encourage and add further support to implementation of telehealth as part of surgery care, especially in elderly patients.
Abstract no. 562 Nurses’ needs on the adoption of hospital IoT-based service
Seung Jin Kang, Hyun Young Baek Eun Ja Jung, Hee Hwang and Sooyoung Yoo, Seoul National University Bundang Hospital, Seongnam-si
Introduction Internet of things (IoT) technologies, which combine a variety of sensors, devices, and networks, within hospitals are expected to increase the quality of patient care, reduce medical costs, enhance job efficiency of the healthcare staff including nurses, and improve hospital environments. Despite the presence of such needs in healthcare, only a small number of studies have investigated which services are in high needs. The present study aims at identifying the needs in healthcare by performing a survey of needs for IoT services within hospitals on nurses working in tertiary hospitals.
Methods Voluntary, paper-based, self-report questionnaire survey was conducted on all nurses working at single tertiary hospital from July 5, 2016 to August 9, 2016. A multidisciplinary team was arranged to extract 15 IoT hospital service items divided into three categories of patient safety, work efficiency, and hospital environment.
Patient safety: Fall management, Pressure ulcer management
Work efficiency: Smart infusion pump, Continuous vital sign monitoring system, Smart patient transfer, Hand disinfection, Rehabilitation management, Vital sign device interface system, Medicine administration monitoring, Patient tracking, Medical staff tracking
Hospital environment: working environment monitoring, device/equipment monitoring, real-time asset tracking, smart lighting system
Needs were measured on a 7-point Likert scale. Extra opinions were allowed to obtain other than 15 IoT service items. In addition, Needs for each item were compared by dividing the nurses into ward nurses and non-ward nurses as well as into nurses with fewer than 5 years of experience and those with more than five years of experience. Significance of intergroup differences was verified using T-tests.
Results Of 1,204 eligible participants, 1,086 participated in the survey (90.2%). There was a high need for all of the service items overall, with at least 5 out of 7 points for each of them. Particularly, vital sign device interface system had the highest demand (mean 6.2, SD 0.93), followed by continuous vital sign monitoring system (mean 6.0, SD 5.8). In terms of wards and non-ward departments, nurses in wards showed high needs for IoT services related to patient care while those in non-ward departments showed high needs for IoT services related to work efficiency. Further, needs for all IoT services increased with increasing career experience.
Discussion This study found that needs for IoT are high overall in healthcare. Despite the limitations of generalizing the study results due to the fact that the survey was only conducted on nurses in a single university hospital, this study is meaningful in that it shed light on the future needs for IoT services within hospitals based on deep understanding and broad experience on ICT technology by conducting a comprehensive needs survey on hospital nurses familiar with digital technology.
Conclusion This study identified needs for IoT services and positive attitudes toward novel technology among nurses working in hospitals. We expect the findings of this study to provide valuable insight for hospitals domestic and abroad for the application of IoT services.
Abstract no. 563 Predicting asthma at age 8: the application of machine learning methods
Silvia Colicino, Cosetta Minelli, and Paul Cullinan, National Heart and Lung Institute, Imperial College, London, London
Alex Lewin, Brunel University London, London
Steve Turner, University of Aberdeen, Aberdeen
Adnan Custovic, Imperial College London, London
Introduction Asthma is among the most common chronic conditions in childhood. We aimed to develop and validate robust statistical models to predict asthma at 8 years of age using three Machine Learning methods.
Methods The data come from 3 UK cohorts in the STELAR consortium. We studied 1,145 children from Ashford and Aberdeen and externally validated the predictive models using data on 348 children from Manchester. Information on characteristics of the children, family related factors and asthma-like symptoms were collected at recruitment and at 1 and 2 or 3 years of age. We defined asthma at age 8 by the presence of at least two of the following: (1) current wheeze (2) asthma treatment (3) a doctor’s diagnosis of asthma the prevalence was 65 (12%), 87 (11%) and 49 (14%) in Ashford, Aberdeen and Manchester, respectively. We developed predictive models using penalized regression methods (LASSO and Elastic Net, EN) and an empirical Bayes regularization method. These models simultaneously perform coefficient estimation and variable selection. The amount of shrinkage towards zero of the regression coefficients is controlled by hyperparameters that were chosen based on 10 fold cross-validation. We used a Normal-Gamma hierarchical prior distribution for the empirical Bayes binomial model in order to account for highly correlated variables. We externally validated these models and assessed their predictive performance by discrimination and calibration measures.
Results The LASSO, EN and empirical Bayes regression models selected 20, 23 and 19 predictors, respectively, from the initial 61. History of parental allergies and doctor’s diagnosis of eczema, the absence of a dog in the house, and antibiotic use at the age of 2 years were found to be important predictors of asthma at 8 years in all predictive models. Other predictors selected include paternal smoking, wheezing symptoms, hospital admissions and birth order. Overall, predictive models showed good accuracy (0.67, 0.64 and 0.69 for LASSO, EN and empirical Bayes, respectively). Sensitivity, specificity and negative predictive value were high (0.84, 0.65 and 0.98 for LASSO, 0.90, 0.59 and 0.96 for the EN and 0.82, 0.67 and 0.97 for empirical Bayes, respectively), whilst positive predictive values (0.28, 0.27 and 0.29 for three methods respectively) were generally low. All 3 methods reported an area under the ROC curve of 80%, showing good predictive performance and favourable discriminative ability to distinguish subjects with and without the disease.
Discussion After validation, our predictive models demonstrated good discrimination ability for asthma. Overall, the empirical Bayes method selects the most parsimonious model and provides better accuracy and predictive ability, at the expense of a lower sensitivity, compared to the other two methods. On the other hand, LASSO and EN provide very similar results with a higher accuracy in the first approach.
Conclusion This multicentre study of asthma-like symptoms in children, combined with novel statistical methods, demonstrates promising results in predicting asthma. The predictive performance in terms of positive predictive value may be further improved with the use of additional predictors and a more targeted population.
Abstract no. 567 Feasibility of electronic quality indicators for inpatient falls based on data from ENRs
Insook Cho, Inha Univerity, Incheon
Eun-Hee Boo, National Health Insurance Service Ilsan Hospital, Gyeonggi-do
Yeun-Hee Kim, Asan Medical Center, Seoul
Soo-Yeun Lee, Inha University Hospital, Incheon
Introduction Most electronic health record (EHR) systems containing electronic nursing records (ENRs) are not based on standards that facilitate semantic interoperability. We hypothesized that reorganizing nursing data into a standard format would allow the sharing and comparison of nursing data across settings. We tested the eMeasure process of the National Quality Forum using nursing data obtained in specific ENR environments, and validated the results based on manually abstracted existing reports. Inpatient fall prevention was selected as a nursing-sensitive quality measure.
Methods This study was conducted in several steps: (1) establishing a project team, (2) developing a data dictionary by reviewing eight international and national practice guidelines, (3) identifying evidence-based data elements and an indicator map, (4) mapping the local terms to concepts in reference terminologies, and (5) representing indicators and validating the process by comparing those obtained by manual abstracting. We used the current definitions of quality indicators for inpatient falls and standard nursing terminologies (the 2015 releases of the Logical Observation Identifiers Names and Codes [LOINC] and the International Classification for Nursing Practice [ICNP®]). The nursing data of 7,829 and 8,199 patients from 2 Korean hospitals with different ENRs were used to represent indicators and validate the process.
Results The identified data dictionary contained 45 data elements that were categorized into 53 concepts. These concepts were mapped onto LOINC and ICNP with coverages of 75.5% and 54.7%, respectively. The indicator map derived from a review of 10 practice guidelines identified 11 process indicators (e.g., the percentage of patients assessed for fall risk within 24 hours of hospital admission, and the percentage of patient days at risk of falling) as well as two outcome indicators (fall incidence and the percentage of falls with injury). These outcome and process indicators could be successfully represented using data from the two ENR systems, but the process indicators were not available for the manual abstractions.
Discussion In this study we were able to quantitatively represent quality indicator matrix in a form that was comparable with that used in other hospitals. The process indicators were not measureable for the manual abstractions. For the hospital that did not have an explicit policy and governance for data structures, this post-implementation solution showed several limits in mapping and reorganizing of data, which were labor-intensive and troublesome. This finding was typically observed when we determine whether the data elements in the data dictionary can be extracted from the two ENR systems. We could find significant differences depending on whether the data element was captured from a structured format or a semi-structured format. Considering the concept mapping with standard terminologies, there were significant gaps. An unexpected finding was a new detection of fall events, not reported to internal reporting system, through the data analysis on narrative nursing notes from the both of hospitals.
Conclusion Reorganizing nursing data from specific ENR environments into a standard format allowed quantitative representations of inpatient falls successfully. This implies that nursing-sensitive outcome measures can be shared and compared throught the utilization of clinical nursing data from multiple ENRs.
Abstract no. 571 Demonstrating the feasibility of using electronic health records in genome-wide association studies: a case study in the UK biobank
Ghazaleh Fatemifar, Michail Katsoulis, Riyaz Patel, Harry Hemingway, and Spiros Denaxas, Farr Institute of Health Informatics Research, Institute of Health Informatics, University College London, London
Introduction Genome-wide Association Studies (GWAS) use cases and controls from investigator-led studies, with cases often defined using manually curated medical record data. Along with the decreasing cost of genotyping, there is increasing demand for larger sample sizes to detect smaller effects. Large scale bio-banking efforts have established cohorts with >100K participants. To define cases in these cohorts it is no longer feasible to use manual approaches, and self-reported data is limited by its lack of accuracy and phenotypic resolution. To overcome these challenges, structured health data through electronic health records (EHR) is increasingly being made available. In this study, we sought to explore the performance of an EHR-derived phenotype of myocardial infarction (MI) for a GWAS in a national biobank, with a view of comparing our findings to published studies using conventional case ascertainment.
Methods The UK Biobank is a cohort with 500K middle-aged participants recruited from England, Scotland and Wales. Genotyping was performed using two Affymetrix Axiom arrays. We applied a previously validated MI phenotype algorithm (https://www.ucl.ac.uk/health-informatics/caliber) using secondary care diagnostic codes from hospitalisation (Hospital Episode Statistics) and mortality (Office of National Statistics) for UK Biobank participants, in a sub-cohort of 112,142 participants who were genotyped as of June 2015, to define participants with prevalent or incident MI (cases) and those without MI (controls). We used logistic regression to test the association between 10 million imputed genetic variants (expected allelic dosages) and MI whilst controlling for the effects of sex, batch, array and centre as well as principle components 1-15. In order to test the validity of our results we extracted all known genome-wide signals for MI and systematically compared these with our results.
Results Within the sample studied, we identified 3,408 MI cases (mean age 62, male 78%) and 108,734 controls (mean age 57, male 47%) derived from EHR. Baseline characteristics for MI cases were similar to those reported in published MI GWAS studies (62% smokers, 60% using statins). QQ-plots showed little inflation of the test statistic (lambda EHR=1.02). After adjustment for covariates we identified 69 variants in two chromosomal regions showing genome-wide significance (<5x10-8). The most robust association was for rs944797 on chromosome 9 (Risk Allele: C OR: 1.16 P: 1.4x10-11) which was a comparable estimate to that identified previously.
Discussion Using EHR to define cases of MI, we were able to replicate several previously reported genome-wide associations, which had used conventional case ascertainment. EHR derived MI cases also had characteristics consistent with those that were expected in traditional cohort studies. We did not identify all known associations but this is likely due to statistical power. Whether an EHR-derived approach for case ascertainment out-performs, a self-reported approach remains to be tested.
Conclusion EHR-derived phenotypes offer a viable alternative to manual phenotyping at a lower cost and at higher clinical resolution and can accelerate advances in precision medicine though large-scale GWAS.
Abstract no. 573 Flipped versus traditional classroom in a small-scale programming course
Floris Wiesman, Tom Broens, and Monique Jaspers, Academic Medical Center, University of Amsterdam, Amsterdam
Introduction The flipped classroom, which involves moving content delivery out of the classroom and spending more time on reflection, can bring about significantly better learning outcomes than the traditional educational model. Large classes in particular seem to benefit. Sceptical lecturers however state that small classes by their nature are already interactive and offer a form of active learning. Moreover, students complain that they pay tuition to hear lectures, not to do homework as in the flipped classroom model. We investigated how a small-scale course can benefit from the flipped classroom model such that the students are satisfied with this new learning model.
Methods The course was an introduction to programming in Java in a bachelor programme of Medical Informatics. We performed a crossover study where the first 7 weeks followed the flipped classroom model and the next 6 weeks the traditional model. The flipped model involved each week (a) 2-4 video lectures (each including 1-3 formative quiz questions) to be watched at home, with an average total length per week of 25 minutes. (b) 90 minutes of “lecture” consisting of 5 formative quiz questions answered with mobile phones. Based on the results the lecturer provided explanations. This took 15-20 minutes. The remaining time was used to do exercises on paper or laptop, with the lecturer and a teaching assistant. (c) 120 minutes of computer lab supervised by teaching assistants. In the traditional approach there were no video lectures. Each “lecture” consisted of 50 minutes of traditional face-to-face lecturing (with students interrupting to ask questions) plus 40 minutes of exercises, which were as in the flipped model but interleaved with the lecture. Computer lab work was unchanged. A paper questionnaire was issued directly following the exam.
Results Of the 50 students who started the course 10 dropped out during the second part of the course. Among the remaining 40 who took the exam, the response rate of the questionnaire was 80%. The reported attendance of the lectures increased from 79% for the flipped model to 85% for the traditional model. The median number of video lectures watched was 24 out of 26. 91% of the students stated they were better prepared for the exercises by the video lectures than traditional lectures. Given the choice between video lectures only, flipped, and traditional model, 81% preferred the flipped approach. In the remarks, 21% stated that the possibility to watch video lectures multiple times and to pause them was valuable.
Discussion Content becomes progressively more difficult during the course, which is a more plausible explanation for the dropouts than the change from flipped to traditional model. Moreover the increasing difficulty may have negatively affected students’ attitude towards the traditional model.
Conclusion Without changing the number of contact hours, we introduced a flipped classroom that was favourably received by the vast majority of the students. Attendance was only slightly lower for the flipped classroom.
Abstract no. 584 Does pay-for-performance improve mental health related patient outcomes? The association between quality of primary care and suicides in England
Christos Grigoroglou and Evangelos Kontopantelis, NIHR School for Primary Care Research, Centre for Primary Care, Division of Population Health, Health Services Research and Primary Care, University of Manchester
Evangelos Kontopantelis, Centre for Health Informatics, Division of Informatics, Imaging and Data Sciences, University of Manchester
Luke Munford, Centre for Health Economics, Division of Population Health, Health Services Research and Primary Care, University of Manchester
Roger T. Webb and Nav Kapur, Centre for Mental Health and Safety, Institute of Brain, Behaviour and Mental Health, University of Manchester
Tim Doran, Department of Health Sciences, University of York
Darren M. Ashcroft, Centre for Pharmacoepidemiology and Drug Safety, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Sciences Centre (MAHSC) and NIHR Greater Manchester Patient Safety Translational Research Centre, Manchester Academic Health Sciences Centre (MAHSC)
Introduction The Quality and Outcomes Framework (QOF), one of the largest Pay-for-Performance (P4P) schemes of its kind, was introduced in 2004, to improve quality of Primary Care in the UK. In this study, we assessed and quantified the relationship between general practice performance on the mental health domain indicators of the QOF and suicide mortality in England for the period 2006-2014.
Methods We obtained practice-level information covering over 99 percent of the registered general practice population and attributed to Lower Super Output Areas (LSOA) in England. Negative binomial models were fit to investigate the relationship between spatially estimated recorded quality of care and suicides. In order to measure quality of care we aggregated all indicators from the two mental health domains of the QOF, i.e. depression and serious mental illness (SMI), into a composite score. Analyses were adjusted for deprivation, social fragmentation, prevalence of depression and serious mental illness, as well as census variables.
Results Overall, no significant relationship was found between practice performance on the mental health indicators of the QOF and suicides in the practice locality (1.00 95% CI [0.99 to 1.00]). Suicides were associated with greater area social fragmentation (1.053 95% CI [1.047 to 1.059]), greater area deprivation (1.015 95% CI [1.014 to 1.016]), increased prevalence of depression (1.012 95% CI [1.003 to 1.021]) and rural location (1.048 95% CI [1.017 to 1.080]). Men aged 40 to 44 had the highest risk of suicide (1.854 95%CI [1.774 to 1.959]).
Conclusions For those practices that participate in the scheme, higher reported achievement of mental health specific activities incentivised in the QOF was not associated with significant changes in suicides. These findings suggest implications for the effects of other similar programmes on suicide prevention.
Abstract no. 586 The quantified outpatient - challenges and opportunities in 24hr patient monitoring
David Infante Sanchez, University of Birmingham, Birmingham
Sandra Woolley, Keele University, Newcastle-under-Lyme
Tim Collins, Manchester Metropolitan University, Manchester
Philip Pemberton, Tonny Veenith, David Hume, Katherine Laver, and Charlotte Small, University Hospitals Birmingham, Birmingham
Introduction Patient monitoring systems capable of accurate recording in the real-world, during the activities of everyday living, can provide rich objective accounts of patient well-being that have broad application in clinical decision support. Combining physiological, environmental and actigraphy sensing together with a quantified subjective patient report and activity log, provides new opportunities and new challenges in big data analysis, data mining and visual analytics.
Method An iterative prototyping approach together with clinical collaboration informed the design and development of a novel 24hr sensing system with broad application relevant to sleep assessment. The system design, sensor selection and visual analytic strategies were informed by literature review and pilot studies with i) clinical staff and ii) healthy participants.
The sensing system comprised, i) a daytime wearable sensing unit (on-body accelerometry for Metabolic Equivalent Task, pulse, skin temperature and resistivity) and ii) two night-time sensing units (an on-body unit as per daytime but with wrist accelerometry, and a bedside unit for ambient light, temperature and sound-level). Continuous recordings were used to generate averages, minima and maxima in 1-minute, 15-minute, 1-hour and 4-hour intervals. For data mining and visual analytics, these records were combined with quantified accounts of subjective user reports and activity logs. Ten subjects (including three clinicians) tested the system for up to three consecutive days and nights and provided assessments of use and comfortability. Five clinicians were interviewed regarding system applications, barriers to use, data use and visual analytics.
Results Data acquisition was successful across a wide range of MET levels. System comfortability was good but with some discomfort and skin irritation arising from prolonged use of a carotid pulse sensor (selected for its robust performance compared with wristband alternatives). Electrooculography sensing for REM sleep detection was attempted but was uncomfortable and performance was unsatisfactory. Usability of the system benefitted from prolonged battery operation. Few data losses resulted from user-administration of sensors, but more resulted from a lack of prototype ruggedisation. Attempts at intuitive multivariate data visualizations, including heat maps, motion charts and clustered views, had limited success. However, the system and approach was assessed as very good for real-life application and decision support.
Discussion 24hr outpatient sensing has wide clinical application in rehabilitation, in the management of chronic conditions and, in pre- and post-surgical assessment. However, better detection of both low level activity and sleep is required than currently available in commercial activity monitoring devices.
Conclusion Multi-modal outpatient monitoring can perform robustly and with acceptable comfortability across a spectrum of activity types and levels, however, system robustness and ease-of-use are paramount to reliability, and users’ self-application of sensors requires careful attention.
The new big un-delineated, multi-modal, multi-dimensional, data spaces created are unfamiliar, uncharted territories that require new understandings, guidance and training. Data mining and visual analytics provide new research insights but there are many challenges regarding their translation in clinical practice.
Abstract no. 594 A tool to improve the efficiency and reproducibility of research using electronic health record databases
Mohammad Al Sallakh and Gwyneth Davies, Swansea University Medical School, Swansea
Sarah Rodgers, Farr Institute, CIPHER, Swansea
Ronan Lyons, Farr Institute, CIPHER, Swansea
Aziz Sheikh, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh
Introduction Interrogation of routine electronic health record (EHR) databases often involves repetitive programming tasks, such as manually constructing and modifying complex database queries, requiring significant time from an experienced data analyst. The objective was to develop a tool to automate the selection and characterisation of cohorts from primary care databases to be used by data analysts and researchers.
Methods We identified a set of common elementary approaches to query clinical variables from the primary care database of the Secure Anonymised Information Linkage databank. We then designed an easy-to-use web-based user interface to allow using combinations of these approaches as ‘building blocks’ for querying more complex variables. We created an R programme to automatically generate and execute the corresponding Structured Query Language (SQL) queries.
Results The developed prototype allows researchers to query clinical information from primary care databases based on the following elementary variable types: (1) count of events of interest (e.g. asthma prescriptions) or their distinct dates (2) the code or date of the earliest or latest event of interest (e.g. type of the earliest smoking cessation prescription) (3) the code or date of the event of maximum or minimum value (e.g. maximum BMI recording ever) and (4) count of events of interest having complex temporal constraints with other events (e.g., count of asthma doctor visits with oral steroid prescriptions within one week). Researchers may choose fixed, dynamic, or individualised query intervals. Algorithms are saved on a web server as versioned and sharable objects. The prototype integrates with a Read Codes dictionary and a sharable codeset repository allowing researchers to keep a record of codes used for reporting transparency.
Discussion The developed prototype provides a scalable, versatile solution for the implementation of complex cohort selection and characterisation algorithms using primary care databases. The automatic generation of SQL queries reduces human errors and should enable rapid and scalable implementation of these algorithms, which has the potential to improve research efficiency and reproducibility. In addition, the graphical user interface allows researchers with no programming skills to interrogate the data. The tool is under active development to improve the functionality and usability, and we look forward to testing it in other databases and assessing its suitability in different research contexts. We plan to make this tool available under an open source licence.
Abstract no. 595 The Manitoba meta-data mapping project
Lisa Lix, University of Manitoba, Winnipeg
Wattamon Srisakuldee, George and Fay Yee Centre for Healthcare Innovation, Winnipeg
Introduction New healthcare treatments such as prescription drugs and surgical procedures are often tested in randomized clinical trials (RCTs) or evaluated in cohort studies. RCTs, in particular, can give an accurate picture of the benefits and harms of new treatments in the short term. But many treatments continue to be used for decades, meaning that RCTs or cross-sectional cohort studies do not provide a full picture of their long-term effects. In the past it was common for data to be archived when a study was finished. There is a worldwide movement to make data available for reuse in order to check the accuracy of original findings, look for new benefits and harms, and measure long-term benefits and harms. The latter can be done by linking the original participant information with data from large administrative databases. Canadian provinces provide universal health care that generates extensive records, which can be linked to the information collected in existing RCTs and cohort studies. The objective of this study is to: (a) describe the process to develop a repository in the province of Manitoba, Canada containing descriptive information (i.e., meta-data) about trials and cohort studies conducted in the last ten years, and (b) identify barriers and enablers of data reuse studies.
Methods Study participants are principal investigators who have conducted a RCT or cohort study that meets the following criteria: (a) the study captures information about one or more of the following health domains: health status, factors that influence health status, health care, public health, and health–related interventions, (b) the study collects data on Manitoba residents, and (c) the health data must come from studies completed between January 1, 2007 and December 31, 2016. Principal investigators were identified via contacts with research offices at all provincial universities and clinical research departments at hospitals, health regions, and related provincial and regional organizations. The collected meta-data includes characteristics of the patients/cohort participants (i.e., age group, sex, disease characteristics), characteristics of the study measures, data custodian/trustee, and willingness of the principal investigator to initiate data sharing agreements and/or participate in data linkage projects. Meta-data are collected using an on-line tool developed with REDCap software.
Results To date, we have identified more than 80 principal investigators who have been contacted to provide meta-data. Data collection is in process. The collected data will be used to establish a publicly-accessible online meta-data repository.
Discussion This study will help to identify the characteristics of study data that could be reused in new investigations, as well as potential methodological, logistical and ethical challenges associated with data reuse. The study results will be used to develop focus groups with members of research ethics boards and research review committees to identify issues associated with investigator requests to reactivate trials and extend cohort studies via record linkage. The study is currently being replicated at two sites in the province of Ontario, Canada to assess the feasibility of implementing it on a national basis.
Conclusion The results from this study will be used to propose best practices for data reuse focusing on data linkage. Collectively, this study will help to impact the reuse of health data in Canada to improve patient care.
Abstract no. 596 Solution for work flow management of surgical operation
Myon-Woong Park and Jae Kwan Kim, Korea Institute of Science and Technology, Seoul
Soo Hong Lee, Yonsei University, Seoul
Introduction In order to enhance the safety and efficiency of surgery, systematic information service to support various stakeholders is necessary. A smart system supposed to comprehend whole procedure of the operation and be able to provide with intelligent service such as proactive data mining and generation of warning at appropriate timing is being developed for the support.
Methods Workflow is a formalized model of a certain operational process. Through the model, the system understands the whole process in advance, reckons current progress according to the contextual information, and provides necessary service in timely manner. IDEF0 was used for the representation of the functions and relations in the model. Information, data, knowledge, context and instances pertinent to the surgical operations have been analyzed and formalized. Through the function deployment, necessary functionalities for the intended software system have been defined.
Results The resulted software named SWORM (Surgical WORkflow Manager) consists of five modules, namely, DB management module, Adaptation module, Surgery Planning Module, Surgery Recording Module, and Visualization module. The mobile application supporting relevant personnel in moving also has been constructed in a hybrid app development environment. This app is written with web technologies and currently runs on Android. At the server side, the SWORM uses HTTP and MySQL. Based on SWORM which is a kind of middle ware, pre-operative, intra-operative, and post-operative services are to be implemented. SWORM can be integrated with legacy systems including EMR or HIS and make itself a platform for applications.
Discussion The system has been applied to the preparation stage of Maxillofacial surgery for trial use, and evaluated by its developers and users. Feedback from a surgeon is the usefulness of setting up and continuous refining of personalized pre and intra operative process. The anaesthesiologist has expected merits in the perspective of safety as operations would possibly be more systematically monitored.
Conclusion SWORM is continuously improved reflecting the feedback from surgical participants, and service modules based on SWORM platform are being added. The objective of workflow management is to maximize the usability of human and material resources and to prevent the medical accident during the entire stages of surgery from pre to post operation. Ultimate goal of the research is the implementation of intelligent agents to support various medical staff.
Abstract no. 600 Patient flow networks and emergency department performance
Daniel Bean and Richard Dobson, Department of Biostatistics and Health Informatics, Institute of Psychiatry Psychology and Neuroscience, King’s College London, London
Clive Stringer, Farr Institute of Health Informatics Research, Institute of Health Informatics, University College London, London
James Teo, King’s College NHS Foundation Trust, London
Introduction Accident and Emergency department (A&E) performance against the UK’s 4-hour waiting time target is a key metric used to assess hospitals. The flow of patients through the hospital as a whole has been identified as a factor affecting A&E performance. A hospital can be considered as a network of wards that are connected when patients are transferred between them. This network science approach enables a global perspective of the complex dynamic flow of patients through a hospital.
Methods Data on A&E attendances, waiting times and patient transfers between 01/12/2014 and 01/07/2016 were extracted from the electronic medical record at King’s College Hospital NHS Foundation Trust. Only transfers for admitted non-elective patients at both hospital sites (Kings College Hospital Denmark Hill, DH, and Princess Royal University Hospital, PRUH) were included. Patient transfers were modelled as a weighted directed graph in which nodes represent wards and edges represent transfers of patients between wards. Edge weights represent the proportion of all transfers in any given day that each edge accounted for. Discharge from the hospital is represented by an edge to a virtual exit node.
Results Overall, the PRUH network consists of 72 nodes (wards) and 921 edges, and the DH network contains 78 nodes and 1531 edges. We identified a “core” set of these edges that are present in the network every month. This core set is a small proportion of all edges (21% for PRUH, 14% for DH), but accounts for the majority of all transfers (91% for PRUH, 85% for DH) and is likely to be critical to the flow of patients through the hospital network. If network-level changes affect A&E performance, the properties of the transfer network on any given day should predict performance the following day. Unsupervised clustering (PCA) of daily transfer networks separated the highest-performing 10% of days from the lowest-performing 10% in both sites. There is also a clear separation of the transfer networks on weekends vs. weekdays for both sites. For DH, the edges that contribute most to the separation of the best and worst days form clear pathways from admission to discharge with consistently higher or lower flow, whereas in PRUH the differences in flow tend to affect more individual wards.
Discussion Since the best and worst performing days can be separated using the properties of the ward network on the preceding day, the network-level changes associated with poor A&E performance could be a driver of performance. This is consistent with the previous suggestion that the occupancy of wards downstream of A&E is a key determinant of A&E performance rather than A&E attendance rates. Analysis of paths through the network shows that a small subset of edges accounts for the majority of patient flow. These pathways could be key targets for efforts to improve efficiency, particularly in times of crisis.
Conclusion Patient transfers within a hospital can be naturally described
Abstract no. 603 Development and validation of various phenotyping algorithms for diabetes mellitus using data from electronic health records
Santiago Esteban, Manuel Rodriguez Tablado, Francisco Peper, Yamila Mahumud, Ricardo Ricci, Sergio Terrasa, and Karin Kopitowski, Servicio de Medicina Familiar y Comunitaria, Hospital Italiano de Buenos Aires, Buenos Aires
Introduction Recently, the progression towards precision medicine has sought the development of large databases allowing to assess the impact of risk factors or treatments in specific subpopulations. This is usually a problem for classical cohorts, given the difficulty of enrolment and follow-up of a large enough number of patients. Even more so is the situation in developing countries, given the usual lack of funds for local research. Electronic health records (EHR) have been proposed as a solution to these two costs problems. Nevertheless, this comes at a price. The quality of data in EHR is usually less than optimal, particularly regarding misclassification errors. Phenotyping algorithms allow, through the combination of different variables extracted from the EHR, to classify patients according to their particular phenotype. Our objective is to compare the performance of different classification strategies (only using standardized problems, rules based algorithms, statistical learning algorithms (six learners) and stacked generalization (five versions)), for the categorization of patients according to their diabetic status (diabetics, not diabetics and inconclusive Diabetes of any type) using information extracted from EHR.
Methods Patient information was extracted from the EHR of the Hospital Italiano in Buenos Aires, Argentina. In order to have a training and a validation dataset, two samples of patients from different years (2005-2015 total n = 2463) were extracted. The only inclusion criterion was age (≥40 <80 years old by 1/1/2005 and by 1/1/2015 for each sample). The sampling was carried out using simple randomization. The training set (2005) featured 1663 patients. The validation set (2015) represented the ∼ 33% of the total sample (n = 800). Four researchers manually reviewed all records and classified patients according to their diabetic status (diabetic: diabetes registered as a health problem or fulfilling the ADA criteria non-diabetic: not fulfilling the ADA criteria and having at least one fasting glucose below 126 mg/dL inconclusive: no data regarding their diabetic status or only one abnormal value). The best performing algorithms within each strategy were tested on the validation set.
Results The standardized codes algorithm achieved a Kappa coefficient value of 0.59 (95% CI 0.49, 0.59) in the validation set. The Boolean logic algorithm reached 0.83 (95% CI 0.78, 0.89). A slightly higher value was achieved by the Feedforward Neural Network (0.9, 95% CI 0.85, 0.94). The best performing learner was the stacked generalization meta-learner that reached a Kappa coefficient value of 0.95 (95% CI 0.91, 0.98).
Conclusion We evaluated the performance of four different strategies for the development of diabetes phenotyping algorithms using data extracted from an EHR from Argentina. The stacked generalization strategy showed the best metrics of classification in the validation set. The implementation of these algorithms enables the exploitation of the data of thousands of patients accurately and reducing costs compared to the traditional way of collecting data for research. Thus, millions of patients from developing countries could benefit from local and specific data that could lead to treatments that take into account all their characteristics (genetic, environmental, habits, etc.) as it is the objective of precision medicine.
Abstract no. 604 Sensitivity and specificity of a rule-based phenotyping algorithm for automatic cardiovascular disease case detection using electronic medical records
Santiago Esteban, Manuel Rodriguez Tablado, and Ricardo Ricci, Hospital Italiano de Buenos Aires, Buenos Aires
Sergio Terrasa and Karin Kopitowski, Servicio de Medicina Familiar y Comunitaria, Hospital Italiano de Buenos Aires, Buenos Aires
Introduction Electronic medical records (EMR) are becoming increasingly common. They show a lot of promise in terms of data collection to facilitate observational epidemiological studies and their use for this purpose has increased significantly over the recent years. Even though the quantity and availability of the data are clearly improved thanks to EMRs, still, the problem of the quality of the data remains. This is especially important when attempting to determine if an event has actually occurred or not. We sought to assess the sensitivity, specificity, and agreement level of a codes-based algorithm for the detection of clinically relevant cardiovascular (CaVD) and cerebrovascular (CeVD) disease cases, using data from EMRs.
Methods Three family physicians from the research group selected clinically relevant CaVD and CeVD terms from the international classification of primary care, Second Edition (ICPC-2), the ICD 10 version 2015 and SNOMED-CT 2015 Edition. Clinically significant signs, symptoms, diagnoses and procedures associated with CaVD and CeVD were included. The algorithm yielded a positive result if the patient had at least one of the selected terms in their medical records, as long as it was not recorded as an error. Else, if no terms were found, the patient was classified as negative. This algorithm was applied to a randomly selected sample of the active patients within the hospital’s HMO by 1/1/2005 that were 40 to 79 years old, had at least one year of seniority in the HMO and at least one clinical encounter. Thus, patients were classified into four groups: (1) Negative patients (2) Patients with CaVD but without CeVD (3) Patients with CeVD but without disease CaVD (4) Patients with both diseases. To facilitate the validation process, a stratified sample was taken so that each of the groups represented approximately 25% of the sample.
Manual chart review was used as the gold standard for assessing the algorithm’s performance. One-third of the patients were assigned randomly to each reviewer (Cohen’s kappa 0.91). Both coded and un-coded (free text) sections of the EMR were reviewed. This was done from the first present clinical note in the patients chart to the last one registered prior to 1/1/2005.
Results The performance of the algorithm was compared against manual chart review. It yielded high sensitivity (0.99, 95% CI 0.938 – 0.9971) and acceptable specificity (0.86, 95% CI 0.818 – 0.895) for detecting cases of CaVD and CeVD combined. A qualitative analysis showed that most of the false negatives were due to terms not included in the algorith (20.4% of the total errors). False positives corresponded mostly to diagnoses that were later on dismissed (43.8%) and due to incidental findings that had no clinical significance (13.27%).
Conclusions We developed a simple algorithm, using only standardized and non-standardized coded terms within an EMR that can properly detect clinically relevant events and symptoms of CaVD and CeVD. We believe that combining it with an analysis of the free text using an NLP approach would yield even better results.
Abstract no. 610 Data quality issues with using the MIMIC-III dataset for process mining in healthcare
Angelina Prima Kurniati, School of Computing, University of Leeds, Leeds & School of Computing, Telkom University, Bandung, Indonesia
Owen Johnson and David Hogg, School of Computing, University of Leeds, Leeds
Geoff Hall, School of Medicine, University of Leeds & Leeds Institute of Cancer and Pathology, St James’s University Hospital, Leeds
Introduction Process mining is a process analytics approach for discovering and analysing process models based on the real activities captured in the event logs of information systems and there is a growing body of literature on process mining in healthcare. One initial challenge for process miners is access to a fine-grained dataset with suitable information for process mining and this is a particularly difficult problem in healthcare given the sensitive nature of health records. Publicly available datasets are one option. MIMIC-III is an open access de-identified health record dataset from the USA with a large number (n=46,520) of patient records. There are more than 120 publications using the MIMIC dataset in journals or conferences, but none of them have used process mining for process analysis. This research aims to assess the opportunities and data quality issues using the MIMIC-III dataset for healthcare process mining.
Method The study applies an established framework for e-health record data assessment using five dimensions (completeness, correctness, concordance, plausibility, and currency) and seven methods. Five of the seven data quality assessment dimensions were applied. These were data element agreement between records of every single activity in different tables, element presence for process mining attributes (case ID, activity name, and timestamp), distribution comparison to the data descriptor, validity check through database queries, and data source agreement of two hospital data sources of MIMIC-III. Log review and gold standards were not applicable because of the de-identified nature of the data.
Results There are 11 events tables in MIMIC-III meeting the minimum requirements for process mining, resulting in a large number of transactional records (n=324,481,146). There are also five dictionary tables, eight definition tables, and two mapping tables that support analysis. Interim results suggest that the data quality of MIMIC-III is strong for the 11 tables, where process mining can be used. We identified several issues with completeness due to missing data elements. Missing timestamps were evident in three tables: CPT EVENTS (82.28% of records), MICROBIOLOGY EVENTS (7.52%), and NOTE EVENTS (15.01%). The latter two tables can be modified for process mining using a transformation for datestamp replacement, but CPT EVENTS which contains Current Procedural Terminology does not contain other information that can be used to impute the frequently missing timestamps. Analysis of CPT events can be supported by information from other tables, such as ADMISSIONS and ICUSTAYS. The poster will present detailed results including graphs and tables of the issues identified.
Discussion MIMIC-III is available to e-health researchers developing novel methods. Data quality is generally high. One important challenge is that the de-identification process included shifting all dates consistently for each patient to randomly distributed future dates. This means that analysis related to a specific timeframe (e.g. weekend vs weekday, daily workload analysis) cannot be done.
Conclusion MIMIC-III dataset can be used for process mining research in healthcare because it is freely accessible, contains detailed information about patient care and supports reproducible research. Some data quality problems were identified but many can be solved using pre-processing techniques.
Abstract no. 611 Making the complex data model of a clinical research platform accessible for teaching
Christoph Rinner, Georg Duftschmid, and Walter Gall, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna
Thomas Wrba, IT Systems & Communications, Medical University of Vienna, Vienna
Introduction For the last 19 years the Medical University of Vienna has been offering a clinical research platform (RDA)1 to securely document and analyze patient-based clinical research data. Besides its clinical foundation, the RDA is also relevant in the medical informatics curriculum at the Medical University of Vienna as it has been the reference point of numerous prototypes developed during master and PhD theses. However, in order to develop or test new features, in-depth knowledge and hands-on experience with the data model are needed and various security, licensing, and organizational barriers have to be passed. To lower the entry barrier for students working with the complex RDA data model, we aimed to set up a locally deployable development environment to evaluate the feasibility and practicability of new features in the RDA data model.
Methods The RDA allows the generic creation of forms and corresponding documents belonging to a specific patient based on the Entity-Attribute-Value (EAV) design.2 We implemented a local development environment with open source software. The data model still holds the central EAV components but was substantially simplified to focus on only those aspects that are most relevant in the teaching context.
Results Only 6 of the original over 150 tables of the RDA data model were reused. User registration, security features, project administration, etc. were not taken into consideration. To process forms, patients, and documents, SQL (PostgreSQL and MySQL), a simple REST interface and the developed web interface are made available by means of the PHP Framework Laravel. The database was preloaded with sample data no patient data from the RDA are used. We evaluate a SMART on FHIR interface using this local development environment.
Discussion Being able to directly access the data model in a locally deployed environment drastically lowers the barrier for testing RDA-related features developed by students. Due to the high quality standards needed in the RDA, new features resulting from student work are always re-implemented by staff of the RDA, hence the disadvantage of not reusing code directly for the RDA is negligible.
Conclusion We reduced a complex data model for clinical research data to the key aspects needed for teaching and provide a locally deployable environment. The entry barrier for students to develop prototypes or test new features based on the data model of the clinical research platform at the Medical University of Vienna is lowered and the transfer of innovative concepts developed by our students to the platform is facilitated.
References
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Abstract no. 613 Developing a standardized minimum dataset for mitochondrial research data – a project outline
Sabrina Barbara Neururer, Department of Medical Statistics, Informatics and Health Economics, Medical University of Innsbruck, Innsbruck
Erich Gnaiger, Department of Visceral, Transplant and Thoracic Surgery, D. Swarovski Research Laboratory, Medical University of Innsbruck, Innsbruck
Verena Laner and Georg Goebel, Oroboros Instruments, Innsbruck
Introduction Mitochondrial research strongly relies on quality control for monitoring and comparing mitochondrial data between individuals and populations. This abstract presents the methodological outline of a novel project aimed at developing a concept for a minimum dataset in order to describe and exchange mitochondrial data between research groups.
Methods The development is based on the MIABIS (Minimum Information About BIobank data Sharing)1 standard which was developed for sharing meta data referring to biobanks and biomaterial collections. Now we intend to evaluate, which basic data is needed to describe mitochondrial meta information and if MIABIS can be applied for this purpose.
Results The project consists of five steps and modifies and extends the 4-step approach for formalizing free-text information published by Neururer et al.2 to fit our research goal: In Step 1 an analysis is carried out for relevant mitochondrial free-text information (e.g. Bioblast,3 MitoPedia).4 Therefore, a so-called definition analysis approach2 is used. Relevant information is marked and definitions are extracted. These definitions serve as input for Step 2, which consist of a typological analysis.2 To identify the relevant data fields of the minimal mitochondrial dataset, the definitions are coded and iteratively abstracted by using qualitative content analysis methods. These data fields are mapped to the concepts offered by MIABIS in Step 3. This step shows, to which extent the MIABIS standard can be extended for this project. The initial concept for a novel mitochondrial data model is developed in Step 4. In order to validate the model, an expert-based evaluation is the main objective of Step 5. This may trigger an iterative process, which will lead to a refinement and re-evaluation of the mitochondrial data model.
Discussion The minimal mitochondrial dataset will serve as a basis for harmonizing mitochondrial information and will increase interoperability of mitochondrial research data. With the development of such a minimal dataset, we contribute to a structured representation of mitochondrial data which leads to enhanced comparability and facilitates data exchange. The strongly structured analysis approach will allow generating reproducible results. Nevertheless it remains to be shown to which extent the MIABIS standard can be applied for creating a mitochondrial data model.
Conclusion The project combines different approaches from interdisciplinary fields of research (e.g. computer science, social sciences) and contributes to the current state of the art of mitochondrial knowledge management to enhance mitochondrial research networking.
References
L. Norlin, M. N. Fransson, M. Eriksson, R. Merino-Martinez, M. Anderberg, S. Kurtovic, and J.-E. Litton, “A Minimum DataSet for Sharing Biobank Samples, Information, and Data: MIABIS,” Biopreserv. Biobank., vol. 10, no. 4, pp. 343–348, Aug. 2012.
S. B. Neururer, N. Lasierra, K. P. Peiffer, and D. Fensel, “Formalizing the Austrian Procedure Catalogue: A 4-step methodological analysis approach,” J. Biomed. Inform., vol. 60, pp. 1–13, 2016.
“Bioblast - Bioblast.” [Online]. Available: http://www.bioblast.at/index.php/Bioblast.
“MitoPedia - Bioblast.” [Online]. Available: http://www.bioblast.at/index.php/MitoPedia.
Abstract no. 615 Tech-no or tech-yes? Insights from older adults on digital monitoring for physical and cognitive health.
Gemma Stringer, Francine Jury, Ellen Poliakoff, Iracema Leroi, and Samuel Couth, The University of Manchester, Manchester
Angela Branson, Clinical Research Network: Greater Manchester, Manchester
Introduction Recent figures from the Office for National Statistics suggest that dementia and Alzheimer’s disease were the leading cause of death in 2015. Early diagnosis of neurodegenerative diseases significantly improves long-term health outcomes, thus one of the key challenges is to improve disease detection at the earliest stage possible. A second challenge is to closely monitor disease progression and fluctuations to enable the most effective therapeutic interventions. Wearable devices, apps and software offer a solution to these problems by unobtrusively collecting individualised data pertaining to physical, cognitive and functional wellbeing over time. What is not clearly understood, however, is whether older adults (either with or without a diagnosis of some form of dementia) are ready, willing and/or able to use these technologies. To address this issue, we report on qualitative data collected from three different projects which ultimately aimed to evaluate the feasibility of using technology for disease detection and monitoring.
Methods The CYGNUS project aimed to use mobile devices and wearable technology to collect outcome measures for people referred to memory assessment services. A technology questionnaire was used to determine readiness for invite into the mobile device monitoring study (n=160). The SAMS project aimed to detect subtle changes in the patterns of daily computer use as a proxy indicator of early cognitive decline. A debrief questionnaire was used to understand their acceptability and preferences of the monitoring software (n=33).
The SKIP project aimed to monitor fluctuations in Parkinson’s disease symptoms through laptop based tasks and passively through smartphone sensor data. Two focus groups were conducted to discuss the use of technology for disease monitoring (n=6).
Results Initial findings from the CYGNUS technology questionnaire suggest that there was large variability between participant’s ownership of/access to smartphones, tablets and/or computers, and also variability in how often they used these devices. In terms of monitoring for disease detection, the questionnaire from the SAMS study indicated that in general participants (all of whom were regular computer users) did not find the software to be obtrusive and many “forgot” that they were being monitored. In terms of monitoring disease progression, the participants from both the SAMS and the SKIP project agreed that they would like to receive feedback on their symptom fluctuations, but there were mixed views on who should control the data and how often feedback should be received.
Discussion Collectively across the three projects there were consistent themes into the acceptability of technological solutions, with most participants willing to be monitored. Many older adults do not own or have access to such technologies, and those who do may not use these frequently enough to capture symptom onset or progression. Control over access to data, privacy and security are also factors to consider.
Conclusions The design and implementation of health monitoring devices needs to consider the requirements and preferences of the individual to ensure adherence and accurate data capture.
Abstract no. 617 Linguistic phenotypes and early cognitive impairment: complementary diagnostic tools with potential for dementia diagnosis
Kristina Lundholm Fors, Department of Swedish, University of Gothenburg, Sweden, Gothenburg
Dimitrios Kokkinakis, University of Gothenburg, Göteborg
Introduction Memory loss and language difficulties are typical symptoms of neurodegenerative diseases including Alzheimer’s disease and other forms of dementia. Recent studies have shown that signs of language deterioration are early and can be identified using linguistic analysis. A cognitive examination is a prerequisite for early identification of dementia and deeper linguistic analysis can play an important role in diagnosis and follow-up. This paper presents the initial stages of a study aiming to identify, extract, analyze, correlate and evaluate linguistic phenotypes from retrospective data. The study is part of the Centre for Ageing and Health (AgeCap, ) where research related to ageing is conducted.
Methods We use data from a longitudinal multidisciplinary population study of women in Gothenburg, Sweden. This study includes epidemiological, clinical and biological data related to psychiatric and somatic disorders in a representative sample of women. 100 women in the study participated in ‘psychiatric interviews”, conducted by Dr Tore Hällström between 1968 and 1969. This data has now been digitized and is being transcribed. Some of these women developed dementia several years later, and since it is known that the degradation of linguistic abilities is a gradual and incremental process that can last for years, we want to investigate whether language characteristics can differentiate the participants in the very early stages of the disease. We will perform automatic analyses of the data, including the extraction of different variables in order to build, test and evaluate classifiers to identify possible indicators for early detection of mild forms of cognitive impairment. Variables will be extracted from the speech signal (e.g. pause lengths use of slow speech,), its transcription (e.g. parts of speech distribution usage of filled pauses such as “um” and “hmm”) and from conversation analysis (e.g. turn taking topic transitions).
Results This study aims to produce knowledge about the subtle linguistic nature of early dementia and how it can be recognized in spontaneous speech interaction. We will adapt and develop language technology methods to analyze recordings and try to identify characteristics that could be used to distinguish people with early, mild cognitive symptoms from normal ageing.
Discussion Instruments that examine the linguistic production are necessary in order to determine incipient language or cognitive abnormalities that precede various dementias and also differentiate from benign, usually age-related cognitive impairment. In our project we will automatically identify and extract a large number of measurable linguistic features and correlate those with medical follow-up data from the investigations that followed.
Conclusion Our study will produce complementary knowledge about the subtle language changes and the nature of early linguistic phenotypes in a large and homogeneous population. The results can be of importance for health professionals who want to quickly diagnose and identify individuals with various forms of cognitive impairment long before serious symptoms become apparent. This way, new and improved cognitive screening tests can be developed that could be used on a large scale in primary care.
Abstract no. 621 Process analysis in cardiovascular disease using process mining
Guntur Prabawa Kusuma, School of Computing, University of Leeds & Telkom University, Bandung, Indonesia
Owen Johnson and Brandon Bennett, School of Computing, University of Leeds, Leeds
Introduction Cardiovascular Disease (CVD) is one of the main causes of premature death and opportunities to improve care include earlier diagnosis, lifestyle changes and clinical interventions. This research aims to identify actual clinical pathway experiences for CVD patients from e-health records in clinical information systems and to use these to link to outcomes. Care processes can be highly dynamic, complex, ad hoc and multi-disciplinary and, as such, represent a challenge for process analysis. One approach is to apply Process Mining methods to discover processes from event logs and this been used successfully in health care. The ability to use Process Mining tools to identify the actual clinical pathways of CVD patients and outcomes will be explored using hospital data from MIMIC-III, an anonymised e-health database from the USA.
Method Data for patients with a range of CVD conditions was extracted from the full MIMIC-III dataset and prepared for Process Mining. Two tools were used to construct process models – the open source tool ProM and a commercial tool Disco. Initial process discovery used the heuristic and alpha algorithms in ProM. Machine learning approaches will be used in the next stage of this work to classify pathways based on patient characteristics and compared to best practice.
Results MIMIC-III has records for a large number of patients (n=46,520) and by following the CVD codes from International Classification of Diseases 9th revision (ICD-9), we identified 72.13% of patients with CVD (n=33,362) accounting for 43,540 episodes of care with unplanned (emergency and urgent) care episodes representing 82.36% (n=35,860) episodes, elective episodes 15.49% (n=6,745) and new born episodes 2.15% (n=935). For the CVD patients there were 543,757 different kinds of diagnoses with the most frequent diagnosis being ‘hypertension’ (n=20,703), followed by ‘congestive heart failure’ (n=13,111). The mean stay in hospital for CVD patients was 7.3 days with the longest stay being 322 days and the shortest being 35 minutes. There were seven cases of patients with more than two years in length of stay which appear to be data entry errors. Analysis of the pathways is ongoing and the poster will present interim results from the investigation.
Discussion This initial work suggests Process Mining can extract CVD care pathways from hospital data. Issues in data quality were revealed and are being investigated in a related project. This work is the beginning of a PhD research and the methods for process mining of CVD pathways developed will be extended to compare clinical practice and effectiveness in different health systems in UK, USA and Australia6.
Conclusion This research is at early stage but the Process Mining approach appears to be feasible and is already surfacing interest results. Further work will explore the opportunities for process mining CVD pathways from UK hospital and primary care data and develop methods for process mining linked to best practice and better clinical outcomes.
Abstract no. 622 Can primary care electronic health records facilitate the prediction of early cognitive decline associated with dementia? A systematic literature review
Maxine Mackintosh, Farr Institute of Health Informatics Research, University College London, London
Introduction Identifying the early stages of dementia is key in care management, clinical trial recruitment and mitigating the impact of cognitive impairment. At present, cognitive tests are most commonly used to investigate early stages of dementia and are often only conducted after initial symptoms of cognitive decline have been identified. There is potential to harness routinely collected data from electronic health records (EHR) to discover markers of early-stage dementia, both in its cognitive and non-cognitive manifestations. However, the extent to which primary care EHR can facilitate earlier diagnosis of dementia has not systematically been examined. We aim to determine the extent to which EHR can be utilized to identify prodromal dementia in primary care settings through a systematic review of the literature.
Method We searched electronic medical databases (including Scopus, Web of Science, OvidSP, MEDLINE and PsychINFO) for potentially relevant studies up to and including September 2016 and written in English. We used the following MeSH search terms: “dementia” (including its subtypes), “electronic health records” (variations thereof) and “primary care”. Additionally, grey literature was searched including reports released by the government, councils and relevant major UK charities.
Results We identified and reviewed 31 studies. In total 35 risk factors and 147 potential markers of early cognitive decline were identified. There was considerable variability across studies as to whether markers were classed as confounders, risk factors, early markers or co-morbidities. Markers predominantly fell within cognitive, affective, motor and autonomic symptoms, prescription patterns of both dementia and non-dementia medication and health system utilization, including type of consultation, frequency of contact and duration. Three studies investigated variation in the markers’ predictive strengths at different time points during the prodromal period of dementia. In the 24 months prior to diagnosis of dementia, gait disturbances, changes in weight, number of consultations, specialty referrals and hospital admissions showed the strongest strength of association with dementia diagnosis. Number of consultations, unpredictability in consulting patterns, such as “Did not attend”, carer and social care involvement showed the strongest strength of association with dementia diagnosis during a longer prodromal period (up to 54 months).
Discussion Tests which specifically investigate cognitive health, such as the Mini Mental State Exam (MMSE) exam, are often only conducted in the period of Mild Cognitive Impairment (MCI) preceding dementia diagnosis, once irremediable damage has occurred. In many cases, these symptoms are conflated with normal ageing, affective disorders, or attenuated by multimorbidities, and are therefore not directly linked to dementia. These results show that there is a broad range of potential markers which could be used to better define prodromal dementia, however very little literature has been published in this area.
Conclusion There is significant potential to use routinely collected data from EHR to investigate and define prodromal dementia. The use of EHR allows us to obtain a more complete understanding of early-stage dementia according to its more commonly investigated cognitive signs, as well as non-cognitive presentations. Understanding the breadth and trajectories in prodromal dementia period will be key in facilitating earlier diagnosis.
Abstract no. 624 Mapping reporting checklist questions against biomedical literature
Haifa Alrdahi, University of Manchester, School of Computer Science, Manchester
Goran Nenadic and Uli Sattler, University of Manchester, Manchester
Andrew Brass, Division of Informatics, Imaging and Data Sciences, School of Health, University of Manchester, Manchester
Introduction Experimental meta-data reporting is a very important field for reproducing and understanding biomedical experiments and results. Diseases caused by parasites, such as Chagas disease, are causing millions of people serious morbidity that might affect their mortality. The genetic background of the host and the parasite used in the experiments, such as the sex of the host, affects the infection outcomes. Checklist Questions (CLQs) have been designed to capture the experimental metadata and evaluate the quality of reporting. Answering CLQs automatically is important for many reasons: CLQs allow to check completeness and clarity of experimental meta-data, and this can be used in the peer-review process. Answers to CLQs can be used to search the relevant literature for meta-data analysis process an efficient way. However, answering the questions automatically is challenging. For example, identifying one species as the answer from many mentions of species requires an automatic understanding of the context the species are mentioned in. The research objectives are to:
1 Explore which kind of CLQs can be answered automatically.
2 Combine Text Mining techniques (TM), Background Knowledge resources (BK) and the article structure to extract the answers.
Methods We used 83 scientific articles from parasitology literature to answer four CLQs automatically: 1- host’s name, 2- host’s gender 3- host’s strain, 4- parasite’s name. The article title, abstract and beginning of the method sections were used to search for the answers. We utilized the CLQs keywords to search the BK and used standard TM techniques while taking into account the structure of the article. Three Named Entity Recognition tools and two large databases were used to extract the answers. We calculated the co-occurrence of the host and its strain in the abstract and method sections to increase the answers confidence.
Results & Discussion
1 The current TM tools and BK resources are not sufficient alone to recognise the correct answers from the extracted entities. For instance, strains have complex nomenclature structure combining capital and small letter with numbers and punctuations. Some texts contain terms with structures similar to the strain, which decreased the accuracy of the extracted strains.
2 The host description (name, strain, gender) is usually found together in 1-4 sentences in the beginning of the method section.
3 Both (host and parasite) or one of them were reported in the abstract and title sections.
4 The host is linked to the parasite with a verb phrase “infected with” or similar in the method section.
Conclusions So far, we can use the frequency of the host and parasite entities in title, abstract and method sections to answer the CLQs reliably. A rule-based model is planned to find the answers using the co-occurrence, frequency rate of the entities and the entities position in the sentences. Moreover, we are interested in parsing the context surrounding the entities because it will help to find the relations between the entities.
Abstract no. 627 The case for a more efficient and effective EHR system: the Portuguese files
Bruno Miguel Oliveira, CINTESIS - Center for Health Technology and Services Research, Faculty of Medicine of the University of Porto, Porto
Rui Vasconcellos Guimarães, MEDCIDS - Community Medicine, Health Information and Decision Department, Faculty of Medicine of the University of Porto, Porto
Luís Filipe Coelho Antunes, FMUP - Faculty of Medicine of the University of Porto, Porto
Pedro Pereira Rodrigues, CRACS, INESC-TEC, Faculty of Sciences University of Porto, Porto
Introduction In medical environments, research and justice there is an anecdote that Electronic Health Records (EHR) are insecure, incomplete, and do not fulfil the needs of individuals and the society. As personal records, EHR are defined and protected by law, and thus we aim to confront the hands-on experience of the professionals with the law, evaluating if issues arise from an incorrect application of the law, or from the law itself.
Methods We conducted: i) a thorough reading of the laws that rule medical and public data, and external organisations that, by their nature, have to have or request access to health records ii) a number of interviews with experts in the medical, research and systems information fields regarding hands-on experience and iii) a nominal group technique (NGT) with forensic experts regarding their practical view on accessing EHR. On the interviews we used content analysis and extracted categories (issues) a posteriori. The categories and the outcomes of the NGT were then triangulated with the law.
Results The categories from the interviews are: no guidelines and protocols on data management lack of data semantics scattered information no defined security and anonymising protocols heavy bureaucracy on external data access and lack of definition on secondary and interchange usage of EHR. The main outcome from the NGT were: low efficiency and effectiveness heavy bureaucracy risks on data anonymity no real-time data access ambiguity on the legitimacy on data access slowness and no means to check if data is complete. The law is clear on who, how and why should access EHR. Defines, by decree, the information that must be registered in medical discharges. It does not define protocols for information storage nor interchange. It is vague on data reutilisation, interchange and anonymising, but defines the office of responsible for the access to information (RAI). Regarding forensic analysis, although clear, it is heavily bureaucratic, with an extensive chain of requests and long response times.
Discussion Many of the issues raised by both the interviewed and the experts on the NGT, security, anonymity, scattered information, slowness, data semantics, no real-time data access, and data completeness arise from the lack of definitions in the law. While the law states that access levels should be defined, in practice it provides no protocol, leaving this task to developers and RAI. This originates all or nothing scenarios, where professionals either have complete access or none. Furthermore, with no protocols each system is free to implement its own, making it difficult to query existing databases. Moreover, the vague definition on data anonymity renders the reutilisation of data unpractical, crippling research. As for data interchange, the law is clearly ineffective. The lack of definitions, coupled with heavy bureaucracy, can originate situations that injure the application of justice.
Conclusions From the analysis performed, we conclude that, although clear in many aspects, in practice the law is both vague and bureaucratic. We consider that the law should define protocols for data flow, interchange, audit and security, enabling EHR systems that serves best both the individual and the society.
Abstract no. 629 Promoting the reproducibility of team health science – distributed analytics under restrictive data policies
Athanasios Pavlopoulos, University of Manchester, Manchester
Introduction Data analysis platforms used for health informatics research, follow a centralised approach to data science. This is depicted as a pool of resources within a tightly regulated but research friendly environment. While this is a positive move for science, its benefits cannot be realised outside the boundaries of this ecosystem. As a result, valuable research resources are underused. Examples of research resources include people, data and methods. Typical implications of this situation include issues with reduced research reproducibility and output. A distributed approach to data science has the potential to offer a more efficient use of research assets geo-distributed around the globe and ease the challenges of research reproducibility and output. However, additional research is necessary to establish how a distributed research environment could potentially replicate and advance the centralised model. State of the art virtualisation technologies are being investigated for this purpose.
Discussion The first contribution of this research is a systematic literature review of data analysis platforms and virtualisation technologies. This highlights the advantages and the disadvantages of the centralised and the distributed approaches to data science, focusing on the various challenges to data analysis that could be eased by using virtualisation technologies. It will describe the challenges that will be tackled in this research and those that will be left for further work.
The second contribution is a model and a framework for the transition from a centralised approach to a distributed approach. It sets the centralised approach requirements and converts them for a distributed approach. This creates a platform that conforms with the regulations while it can use more resources and enable better research practices.
The third contribution is an evaluation method that measures the effectiveness of the solution. This is based on a scale of easing the barriers to data science and good research practices. It measures the effect of the solution in performing data science on geo-distributed resources and in allowing third parties to reproduce the research and increase the research output.
A case study research paradigm is followed throughout this research. First, an existing centralised approach to data science is studied. Second, proposals for distributed approaches to data science are investigated. Third, the findings from both approaches are merged together to convert an existing centralised approach to a distributed one. Fourth, the evaluation method is created and realised. Domain experts are used throughout the research to align the research findings with state of the art theories and practices.
Conclusions The examination of the case studies from both the centralised and the distributed approaches, highlight the importance of the problem and the soundness of the proposed solution. The model and the framework from the case studies will be realised, together with the evaluation method and its implementation.
Abstract no. 630 REACT (REal-time Analytics for Clinical Trials) supporting clinical trials at the Christie hospital through the iDECIDE framework.
Jennifer Bradford and Donal Landers, AstraZeneca, Macclesfield
Introduction REACT, developed by AstraZeneca, is revolutionising clinical data interpretation and visualisation in ongoing clinical studies. It provides experts with real-time access to integrated clinical trial data such as safety, exposure, efficacy and biomarkers. This enables more informed reasoning, informed decision making and an earlier understanding of the patient benefit-risk trajectory
Methods ‘iDecide’ is a five-year collaboration harnessing clinical informatics to deliver personalised healthcare for cancer patients. The collaboration is between AstraZeneca and the Manchester Cancer Research Center (Cancer Research UK, the University of Manchester and The Christie). As part of this REACT will be used to capture and integrate clinical trial data in real time to support complex clinical and scientific questions.
Results One example of how REACT can impact patients will be through helping to address the challenges in the BISCAY study. BISCAY is an Open-Label, Randomised, Multi-Drug, Biomarker-Directed, MultiCentre, Multiarm Phase 1b Study in patients with Muscle Invasive Bladder Cancer (MIBC) who have progressed on prior treatment (BISCAY). The objective of BISCAY is to explore predictive value of common molecular aberrations in MIBC and assign patients to a cohort with the best chance of benefit.
Discussion Within the BISCAY study REACT will be used firstly to help fully understand the toxicity of the immuno-therapy and target therapy combinations as there is little or no preclinical data available, moreover it will help identify the most efficacious combinations quickly.
The iDecide framework more generally will migrate, further develop and enhance the REACT platform that has been developed in AstraZeneca within Cancer Research UK. Within Cancer Research UK REACT will support oncology studies more widely and ultimately positively impact a greater number of patients.
Abstract no. 635 European comparison of spinal surgery hospitalizations from 2010 to 2013 according to patient profiles
Pascale Brasseur, Medtronic, Tolochenaz
Cecile Blein, Lucie Deleotoing, Camille Amaz, Charlene Tournier, and Alexandre Vainchtock, Heva, Lyon
Introduction This study was performed to compare hospitalizations for spinal surgery development across France, Spain, Germany and Belgium from 2010 to 2013 and to analyze patient’s characteristics.
Methods A retrospective analysis was conducted from hospital databases PMSI for France, CMBD for Spain, SHI for Germany and RHM for Belgium between 2010 and 2013. All spinal surgery hospitalizations were collected based on procedure codes according to the respective classification of each country (CCAM for France, ICD-9 for Spain and Belgium, and OPS for Germany). The mapping of ICD-9 and OPS codes was undertaken from the French procedures. Standardization of rates of spinal surgery patients were based on age from the EU population.
Results In 2013, crude rates of hospitalized patients with spinal surgery were 6.43 per 10,000 in Spain, 18.95 per 10,000 in France, 66.27 per 10,000 in Germany and 67,50 per 10,000 in Belgium. All countries experienced an increase of this number of patients from 2010 to 2013: +14% in Spain, +17% in France and +18% in Germany except for Belgium with a decrease of 13%. The gender distribution was similar between all countries except for Belgium, with slightly more women treated in Spain and Germany: 52% versus 50% in France. However, in Belgium the number of men treated is more important (64%). Mean age was lower in Spain (53 ± 16 years) France (54 ± 17 years) and Belgium (52 years) than in Germany (59 ± 15 years) the [70-80] year group was overrepresented in Germany (24% of patients versus 15% in France and Spain) to the detriment of the [30-40] year group (6% of patients in Germany versus 15% in France and Spain). The age standardized rates of spinal surgery patients were higher in Germany (60.55 per 10,000) and Belgium (67.36 per10,000) than in France (19.91 per 10,000) and Spain (6.53 per 10,000).
Conclusions Between 2010 and 2013, spinal surgery was marked by a progression of more than 14% in each country except for Belgium with a decrease of 13%. The standardized rate of spinal surgery patients varied significantly between the 4 countries, Germany and Belgium having the highest.
Abstract no. 636 Movefit: a healthy lifestyle application
Trond Tufte, University of Bergen, Department of Information Science and Media Studies, Bergen
Ankica Babic, Dept. for Information Science and Media Studies, University of Bergen, Bergen, Norway
Introduction According to World Health Organization the worldwide prevalence of obesity nearly doubled between 1980 and 2008, emphasizing its level of significance. Obesity is often resulting from a sedentary lifestyle, which is also often connected to chronic diseases such as cardiovascular diseases and diabetes, but also mental issues such depression and anxiety. The sedentary lifestyle and its down sides are being addressed by innovative use of mobile health (mHealth).
Methods During the current project a mHealth tool for smart phones has been developed using Design Science Methodology, where the goal has been to promote an active lifestyle. This has been done by implementing social and physical activity stimulating features. The physical activity features consists of e.g. an activity alarm that prompts the user to move whenever the user has been inactive for a certain amount of time. The user will be rewarded points by moving. Another set of features is the ability for a user to create activities or routes. The user will earn points based on the various activities created. These activities can be used and reviewed by others in the same area and is therefore location based. The points earned are being used as a competitive incentive. High activity leads to more points. The various users are being listed on a leader-board with their respective scores which is also based on location. There is also a leader-board for the various routes in the area. They are displayed based on their rating and distance away from the user. Furthermore, there are some social features that allows the users to find other people in the area, where the idea is that the users can physically meet up and do activities together. The users are able to communicate via messages in the app as well as creating their own social profile which is available to people in the nearby area. The application has been developed for Android devices by use of Xamarin, which is a cross platform development tool. The application’s back-end consists of Azure Cloud Services, where both SQL database and server is provided by the host. Evaluation. The application has been evaluated by regular and expert users in order to meet usability requirements. In addition a field expert and a focus group have contributed towards the application’s potential to increase physical activity.
Results There is enough data collected by the app to document its good effect it was possible to demonstrate that the app was capable of promoting physical activity. User testing has also shown the appreciation of the various features such as social networking, activity monitoring, and route/activity creation.
Discussion & Conclusion There are many ways this app can be further developed in order to tailor suit specific user needs e.g. patient activity tracking which can be used by mental therapists or physicians to help motivate the patients. Long term effect of the app has yet to be probed in a different setting e.g. a clinical trial.
Abstract no. 641 Radiology physician order entry system for improving quality of care at a tertiary teaching hospital
Mohammad Yusuf, Aga Khan University Hospital, Karachi
Introduction Aga Khan University Hospital is a JCIA accredited tertiary healthcare facility. To improve quality of care and to eliminate patient identification errors that may arise in the workflow when a physician writes and Imaging order and the patient is registered for the procedure, an Order Entry system was developed within the institution. The system is available to physicians who enter imaging orders. These then are managed within the Radiology department to register the patient and carry out the requested procedures.
Method Ordering radiology procedures for the specific patient needs to be carefully monitored and tracked. For a nonintegrated, paper based system, the chances of errors in listing the incorrect patient identifier on the request slip increases manifold. The electronic POE system now ensures that the intended patient is selected from the system and the radiology procedures intended for that specific patient are requested. As most radiology procedures involve ionizing radiation, a procedure carried out on an incorrectly identified patient will result in unnecessary radiation exposure. Completely avoiding such mis-identified patients and ensuring patient safety by preventing incorrect exposure to radiation etc. increases patient safety. The system was developed and integrated with the Hospital Information System (HIS) and the Radiology Information System (RIS). It was deployed in a phased manner within the Inpatient areas and then in the Emergency Department.
Results The project ensures that correct patients are identified and intended procedures are requested for them. Carrying out correct procedures on the right patient eliminates any clinical and radiation safety issues altogether and quality of medical care given to the patient is improved. The project assures better patient safety, especially in the radiation related environment. It also facilitates the physicians in selecting the correct procedure by traversing an organized hierarchy of procedure lists.
Discussion The project originated based on the need to reduce any patient identification errors and for ordering the right tests for the right patient with traceable documentation. An online Radiology Electronic Physician Order Entry System (RePOE) was developed in-house, closely integrated with the hospital’s HIS and RIS. The system is deployed for inpatient areas and future deployments will include ED and outpatient areas as well. The physicians are able to select the patient on a central patient care portal, select the necessary exams, allergies, transportation, etc. as well as provide additional information regarding the requested procedures. As the patients are selected from the HIS, errors in incorrect identification are eliminated which directly translates to quality patient care.
Conclusion The electronic Physician Order Entry system is one of the essential systems in any HIS environment that has a direct bearing on patient safety and the quality of care. Globally, such systems are not commonly deployed and they are generally classified as those systems that are difficult to implement with the necessary effective change management. Successful development and deployment within the inpatient areas provides a sound testimonial to the project itself.
Abstract no. 642 Exploring multimorbidity using bayesian models with time-based abstractions
Carla Silva, Mariana Lobo, and Pedro Pereira Rodrigues, CINTESIS - Center for Health Technology and Services Research, Faculty of Medicine of the University of Porto, Porto
Introduction Modelling complex disease systems can accelerate the development of productive strategies for identifying factors of multimorbididy. Multimorbity refers to the case of coincident event of a patient expressing more than two diseases simultaneously which, with an ageing population and better chronic diseases management, happens now more often. Given the possible interaction of diseases, the analysis of such conditions requires a dynamic modelling using temporal approaches. Our aim is to propose time-varying abstractions of probabilistic graphical models that can better describe the complexity of multimorbidity in a specific subgroup of patients with acute myocardial infarction (AMI) since these patients are likely to experience multiple comorbidities and develop new conditions.
Method A retrospective cohort of 500 patients admitted with AMI in Portuguese hospitals was included and followed for a maximum of 5 years, yielding a total of 893 hospitalisations and re-hospitalisations after AMI. We defined temporal abstractions, monitoring the occurrence of comorbidities within yearly periods after AMI. Univariate, bivariate and Dynamic and Temporal Bayesian network analysis were conducted on both the hospitalisations and the patients, either by using a) the yearly time-frame temporal abstractions, and b) the full follow-up time. We expose the cohort characteristics and the Bayesian networks to analyse different views of multimorbidity evolving with time. Comparison of different time models was achieved by typical measures in clinical research (e.g. sensitivity/recall and positive predicted values/precision) and the structural Hamming distance, while each comorbidity was finally abstracted using survival analysis.
Results Our preliminary analysis showed that AMI is more commonly located in heart surface other than inferior or lateral. Moreover, atherosclerosis and hypertension, were the most common conditions, while, protein-calories malnutrition, metastic cancer and acute leukemia, trauma, major psychiatric disorders, liver and biliary disease were observed infrequently. There were decreasing statistically significant associations between comorbidities in an abstraction by time events-based, and a constant number in a time intervals-based abstraction.
Discussion Bayesian modelling applied to multimorbididy arises from the need to develop, create and extract advanced knowledge regarding the modelling of diseases. Therefore, we focus on the inference of multimorbidity scenarios expressed in time. We noted different behaviours in multimorbidity assessment, when assuming different temporal abstractions, which might lead to more accurate research paths in the area.
Conclusion An abstraction in a time intervals-based is closer to the representative abstraction of the group in consideration. We came to the conclusion that the suggested data mechanisms can therefore be used to explain series of developments of multimorbidity.
Abstract no. 645 Building an informatics environment to track and monitor CT radiation dose for improved patient care
Mohammad Yusuf, Aga Khan University Hospital, Karachi
Introduction Aga Khan University Hospital is a JCIA accredited tertiary healthcare facility. Among a suite of other high end modalities, it has two Computer Tomography (CT) facilities comprising of 64 and 640 slice machines. The Informatics team identified a quality initiative to track and manage the CT radiation dose. An Informatics based environment was built up to extract, store, integrate and manage the Radiation dose information. This Informatics led initiative resulted in identifying the areas of improvement and radiation dose was reduced to international best practices.
Methods An integrated system was built up to track and manage the radiation dose for all CT examinations. The new CT technology allows various facilities to reduce the radiation dose to patients while retaining good the image quality. The compiled results showed a margin of improvement that resulted in this project. International standards were compared and the protocols were managed to reduce the dose levels. The reduction in radiation dose provides a huge benefit to the patients, especially the paediatric population undergoing CT examinations as it reduces the risk of incidence of cancer due to exposure of radiation causing ionization. CT Dose information was extracted from the standard dose reports from the imaging archive. These were then integrated with the patient demographics and the Hospital Information Systems.
Results The main benefit of the project was to improve patient care and ensure that the patient is exposed to only a minimum level of radiation dose for any CT examination. No cost impact is related to the project except for the backend IT resources. Proper planning and analyses was carried out to define the requirements and to build up an integrated system. The initiative led to the development of improved protocols, adherence to best practices and in lowering of radiation dose in some specific procedures to the recommended international standards. The results were significant and measurable. The changes in CT protocols have resulted in reduction of the radiation dose to the patients.
Discussion The role of Informatics has been pivotal in bringing the Radiology department to the workflows and serviced in the digital age. Informatics related initiative led to the planning and development of the CT radiation dose tracking and monitoring system. It helped in streamlining the CT workflows, develop better protocols, fine-tune the systems in collaboration with the vendors and finally build up a comprehensive dose tracking and management system. This has led to the reduction in radiation dose in specific procedures and improve overall patient quality of care.
Conclusion The project directly impacts a patient and improves the quality of care by recognizing that reducing radiation dose is beneficial for the patient and then directly making efforts to reduce the dose appreciably. As it is important to process in a phased manner to ensure that image quality is retained while reducing the radiation dose, initial results have reduces the dose by about 15%. Subsequent phases will further reduce the radiation dose level.
Abstract no. 646 Discharge abstract data quality changes over time: comparing validity of 2003 and 2015 ICD-10 CA coding of charlson and elixhauser conditions, and adverse events
Cathy Eastwood, Danielle Southern, Danielle Fox, Olga Grosu, Ellena Kim, Chris King, Nicholas vanKampen, Natalie Wiebe, and Hude Quan, University of Calgary, Calgary
Introduction The World Health Organization (WHO) has been developing the eleventh version of the International Classification of Disease (ICD-11), to enhance the data captured from hospital records. As one of three WHO Collaborating Centres (with the Mayo Clinic and Stanford University in the USA), we are testing the “fitness of ICD-11” for improvements before full adoption by the WHO in 2018. As a first step, we will assess agreement between ICD-10-CA and chart review using ICD-11 concepts of medical conditions. Using coded administrative health data for research requires an assumption that the validity of the conditions’ coding is stable over time. Previous work1 showed that the implementation of ICD-10 coding has not significantly improved the quality of administrative data relative to ICD-9-CM. Therefore we will perform an assessment of the validity of ICD-10 data, as coders have gained experience with the coding system. We will test this assumption by studying the temporal trends of coding for multiple conditions in the Canadian hospital Discharge Abstract Database. The objectives were: 1) To compare trends in coding of conditions over time, and 2) To compare ICD-10-CA coded data to ICD-11 concepts through chart review to assess potential improvements to the classification.
Methods To date, we reviewed 1400 of 3000 randomly selected inpatient health records from 2015 from three teaching hospitals in Calgary, Canada, for the Charlson and Elixhauser conditions and 18 categories of adverse events. These hospital records were previously coded using ICD-10-CA the chart reviewers were blinded to the ICD-10-CA coding. Reviewers are identifying conditions as defined by ICD-11 Beta. Validity of ICD-10-CA coding in 2003 will be compared with the validity of ICD-10-CA coding of 2015. Trends in validity over time will be reported. Conditions coded in ICD-10-CA will then be compared to those captured through chart review in ICD-11 Beta.
Results The current health record review will produce a rich and robust database upon which to validate both ICD-10-CA and ICD-11 coding. Sensitivity, specificity, positive predictive value and negative predictive value of ICD-10-CA will be calculated for the multiple conditions, with chart review as the reference standard on the 2015 set of records. Comparison with ICD-11 chart review will provide an assessment of the improvements that ICD-11 will provide for the coding of conditions and adverse events.
Conclusion This study will highlight potential changes in validity of ICD-10-CA in recording the Charlson and Elixhauser conditions and patient safety indicators over a 12-year period. The highest possible data quality is essential for identifying disease prevalence, trend analysis for chronic disease surveillance, and health services planning. Recommendations for ICD-11 based on findings from this extensive validation study, will be communicated to the WHO.
Reference
Jiang, J., Southern, D., Beck, C., James, M., Lu, M., Quan, H. (2016). Validity of Canadian discharge abstract data for hypertension and diabetes from 2002 to 2013. CMAJ Open 2016. 4(4). DOI:10.9778/cmajo.20160128
Abstract no. 647 Assessing the association between different patient indexing strategies and effective indexing during the implementation of an electronic medical records system in the public health system of buenos aires, Argentina.
Santiago Esteban, Leandro Alassia, Analia Baum, and Cecilia Palermo, Dirección General de Informática Clínica, Estadística y Epidemiológica, Health Ministry, City of Buenos Aires, Buenos Aires
Fernan Gonzalez Bernaldo de Quiros, Jefatura de Gobierno de la Ciudad Autónoma de Buenos Aires, Argentina, Buenos Aires
Introduction Within the process of implementing an electronic medical records system (EMR), the creation of a master table of patients (MTP) is an essential step. Starting in January of 2016, the Ministry of health of the city of Buenos Aires is implementing the computerization of the medical records in the public health system. In this process, several patient indexing strategies have been adopted by each primary care health centre according to the features of the centre and its population. Thus, we decided to evaluate the association between the different patient indexing implementation strategies and the rate of effective patient indexing.
Methods Prospective cohort using data extracted from the MTP and the EMR. We included all persons registered in the system between 2016-06-01 and 2016-11-24. The patient indexing implementation strategy was agreed upon with the chair of every health centre. Then, these strategies were grouped in three modalities according to the intensity and methodology used: low intensity: patient registration is an alternative instance to the usual medical care process. It depends on the availability of time of the administrative workers. Paper medical records are predominant. Intermediate intensity: registration is offered mostly to patients who request appointments through the computerized system or those who visit the centre for non-medical purposes (acquiring instant formula or process related to social security). Paper and electronic records coexist. High intensity: the indexing process is proposed as a condition in all instances of consultation at the health centre. As result, we assessed the time since the registration to the first visit registered in the EMR. This was done since the crude indexing (total number of indexed patients) can reflect many people who are indexed but who do not seek medical care. The unadjusted rates of effective indexing were estimated using the Kaplan-Meier method. The curves were compared with the Log Rank test. For the multivariate adjusted model, we used Cox’s proportional hazards regression.
Results The crude analyses showed a significant difference between the curves (p < 0.0001). In the multivariate analysis, many variables violated the proportional hazards assumption, even the exposure variable. This was resolved by creating interaction terms with a flexible function of time for the covariates. For the exposure, a segmented time analysis was used, creating seven day intervals within which, the assumption held. The hazard ratios (HR) of high and low intensity interventions showed on average values above 1 from 0 to 90 days compared to the intermediate intensity strategy (High:2.08 (1.65,2.52) Low:2.59 (2.29,2.9)). From that point on, the HRs of both strategies were not different from 1.
Conclusion Promoting indexing in instances not related to healthcare yielded the worst results in regard to effective indexing. This probably points towards the importance of the medical staff being involved even in the patient indexing process. The results of our study provide us more information in order to discuss the pros and cons of the available indexing strategies with the health centres’ authorities in future implementations.
Abstract no. 649 Assessing the association between age and the probability of being indexed in a master patient index within the process of implementing an electronic medical records system in the public health system of buenos aires, Argentina.
Santiago Esteban, Manuel Rodriguez Tablado, Francisco Recondo, and Analia Baum, Dirección General de Informática Clínica, Estadística y Epidemiológica, Health Ministry, City of Buenos Aires, Buenos Aires
Fernan Gonzalez Bernaldo de Quiros, Jefatura de Gobierno de la Ciudad Autónoma de Buenos Aires, Argentina, Buenos Aires
Introduction The health ministry of the city of Buenos Aires (CABA) has set out to computerize its public health system. The process started at the primary care health centres (PCHCs). One of the critical steps of this project is the creation of a master patient index. Concerns were raised in regard to this indexing process (registration process of personal and demographic data as well as identity verification) possibly interfering with the accessibility to healthcare. One hypothesis is that indexing does not occur at random but rather it was conditional on certain patients’ characteristics, age being one of those factors. Given the importance of the registration process, we decided to investigate the association between patient age and the probability of being indexed.
Methods We included all patients who consulted at three PCHCs (No. 5, 7 and 29) during the months of June and July of 2016, and we evaluated the association between age and the probability of being registered. We adjusted for covariates like age, sex, place of residence, PCHCs of consultation and nationality.
Results We identified 4477, of which 1464 were indexed. The mean age was 14 in the non-indexed group (NIG) and 13 in the index group (IG) (p = 0.578). Of the NIG, 42.7% were between 0 and 10 years old, 17% between 11 and 20 years old and 40.3% older than 20 years old. Of the IG 45.8% were between 0 and 10 years, 12.5% between 11 and 20 years and 41.7% over 20 years. Multivariate analysis, adjusting for potential confounders, showed that patients younger than 11 years of age had a 1.8-fold odds ratio of being indexed compared to patients older than 10 years (p <0.0001), with no difference between CPHC of attention.
Discussion These results support the initial hypothesis that the registration does not arise from a random process, with age being one of the most determinant variables. This could be due to distrust of some adults to share their personal data. We also understand that resistance to registration may be related to the fact that we are evaluating the first few months of a novel process.
Conclusion The results seem to indicate an association between the age of the patients and their probability of being registered. These data allow us to plan future research to clarify the causes of differential registration and strategies to solve it.
Abstract no. 653 Audit and feedback in primary care effectiveness and informatics
Maxime Lavigne and David L. Buckeridge, McGill Clinical & Health Informatics, Department of Epidemiology and Biostatistics, McGill University, Montreal
John B. Hughes, Faculty of Medicine, McGill University, Montreal
Introduction Audit and feedback (A&F) promotes the adoption by clinicians of evidence-based findings and aims to improve healthcare quality. It tries to influence their behaviour in order for them to establish practice patterns leading to improved performance and better health outcomes. A&F is a good candidate for computerization due to its reliance on information processing. Although feedback interventions are effective in other fields, in healthcare they produce moderate effects with variable results. Exploring the cause of this limited effectiveness is difficult due to poor reporting of the theoretical basis of interventions. This study aims to identify the barriers to effective computer-assisted A&F interventions in healthcare and to suggest how informatics methods implemented in the context of theories of behaviour change (BC) can help to overcome these barriers.
Method This study used a qualitative, explanatory case-study design applying deductive thematic content analysis to computer-assisted A&F interventions identified through a review of the literature. After reviewing 456 A&F studies, qualitative data was collected from four documents using a coding scheme developed by combining multiple theoretical perspectives and adjusted to allow the extraction of resource utilization content. The codes were synthesized into representative themes which identified possible determinants of effectiveness and framed them in the context of their deployment as integrated informatics system.
Results The thematic analysis led to the identification of six overarching barriers to effective A&F implementation: “Resource Constraints” includes limitations related to the additional costs and labour required “Diffusion of Information” refers to issues related to the adoption and use of new technologies “Clinical Governance” or the expectation of A&F systems to integrate within existing organizational planning and quality improvement efforts “Dynamic System and Control Theory” addresses how the causal mechanisms of A&F can be used to drive design choices “Cognitive Biases and Behavioural Economics” relates to how real users differ from theoretical rational agents, and how this can affect A&F interventions and “Learning Culture” underlines the importance of fostering the right culture in order to drive sustainable change.
Discussion Qualitative content analysis has limited abilities to explore beyond what the authors chose to include. It however mitigates the effect of heterogeneous reporting on data extraction and allows the generation of richer accounts of the cases. The use of a case-study design enabled us to stay true to the context in which A&F took place. It has produced a holistic view that would be difficult to capture through experimentation or surveys. Focusing on actionable evidence meant the barriers and themes identified are well suited to driving new A&F interventions.
Conclusions Using qualitative content analysis and a multi-theory approach, we identified a set of principles for effective A&F design. We found that informatics facilitated the development of A&F and improved compliance with the proposed principles. This finding suggests that the effectiveness of computer-assisted A&F could be improved through careful application of the identified principles and that computer-assisted A&F is different enough from other types of A&F to warrant separate evaluation. More evaluation is needed as to the effects of the principles.
Abstract no. 656 On the variability patterns in general practitioner prescribing behaviour
Magda Bucholc, University of Ulster, Intelligent Systems Research Centre, Londonderry
Maurice O’Kane, Healthcare Analytics Limited, Portadown
KongFatt Wong-Lin, Altnagelvin Hospital, Western Health and Social Care Trust, Londonderry
Introduction Most interest in the variability in drug prescribing behaviour has been focused on cost saving. It has been estimated that £200 million could be saved if unwarranted variations in prescribing activity were reduced and the drugs were prescribed with the same standard. Such variation indicates the need to focus on efficiency and appropriateness of clinical practice and to examine the possibilities that a large variation might be related to inappropriate prescriptions. Our work examines the change in variability of primary care drug prescribing rates in Northern Ireland’s Western Health and Social Care Trust and investigates its relationship with laboratory test ordering rates.
Method The GP prescribing data (Apr 2013 – Mar 2016) for 55 general practices within the Northern Ireland Western Health and Social Care Trust was obtained from the Business Service’s prescribing and dispensing information systems. The total number of test requests was collected from the laboratory databases of the Altnagelvin Area Hospital, Tyrone County Hospital, and the South West Acute Hospital. Both the number of drug prescriptions and laboratory tests requested in each practice was normalized by the number of registered patients. The variability of drug prescribing data was determined by calculating the coefficient of variation (CV). The degree of correlation between the laboratory test ordering rates and drug prescribing rates was assessed by calculating the Spearman’s correlation coefficient (R).
Results We observed pronounced differences in drug prescribing rates among general practitioners. The high inter-practice variability in drug prescribing behaviour was shown to be caused by several GP practices with abnormally high ordering rates. No correlation between the total standardized number of prescriptions and the most commonly requested laboratory test (electrolyte profile) was reported (R = 0.107, 0.245, and 0.220 in 2013-14, 2014-15, and 2015-16 respectively). In addition, the strength of association between the most common medications used to treat under- and over-active thyroid (i.e. carbimazole, propylthiouracil, levothyroxine, and liothyronine) and the standardized laboratory test requests for thyroid profiles (FT4 and TSH) was found to be very weak (R = 0.028, 0.017, and 0.037 in 2013-14, 2014-15, and 2015-16 respectively).
Discussion There is clearly variability in prescribing rates between general practices, suggesting that the costs of prescription could potentially be lower if the variation is reduced. However, since higher variability does not necessarily suggest lower quality practice, it requires further inspection to determine if the patient population associated with specific GP practices is different and have different needs. The lack of correlation between the prescription rates and requesting rates for laboratory tests shows that practices that request laboratory tests at relatively higher or lower rates than average are not necessarily the ones with higher/lower prescription rates. This implies that other factors may influence GP’s decisions tendency to prescribe or some laboratory tests are simply ordered inappropriately.
Conclusions Our investigation of variability in drug prescribing rates between general practices provides valuable information on practice variation and helps prioritise future research studies to improve the quality of prescribing. We suggest that optimisation of prescribing could be enhanced by conducting appropriate clinical interventions.
Abstract no. 657 Investigating the accuracy of parent and self-reported hospital admissions: a validation study using linked hospital episode statistics data
Leigh Johnson, Rosie Cornish, Andy Boyd, and John Macleod, University of Bristol, Bristol
Introduction The Avon Longitudinal Study of Parents and Children (ALSPAC) is a large prospective study of around 15,000 children born in and around the city of Bristol in the early 1990s. Participants have been followed up intensively since birth through questionnaires, clinics and linkage to routine datasets. In 2013 ALSPAC extracted information on a pilot group of consenting participants from the Hospital Episode Statistics (HES) database. The aim of this study was to validate parent-reported and self-reported data on hospital admissions against HES-recorded hospital admissions.
Methods Subjects included in this study were 3,195 individuals enrolled in ALSPAC who had consented to linkage to their health data before the end of 2012. In nine questionnaires completed when the children were aged between 6 months and 13 years old, parents were asked if their child had been admitted to hospital in the time since the issue of the previous questionnaire (the periods covered varied, ranging from 6 months to 4 years). Additionally, when the participants were 18 years old they were asked whether they had been involved in a road traffic accident in the past year and, if so, whether they had been to accident and emergency (A&E) or stayed overnight in hospital. We compared this information to data recorded in the HES database for the corresponding time period and calculated sensitivities, specificities and predictive values of the questionnaire-reported admissions and A&E attendances, using HES records as the reference standard.
Results Up to 10% of individuals had been admitted to hospital during the time periods covered by the questionnaires. Among those whose parent reported a hospital admissions, at least 60% had one or more corresponding admission in the HES data. Where a hospital admission was not indicated on the questionnaire, an admission was found in the HES data for between 1.4% and 3.6% of the participants. Initial analysis suggests that some of the parent-reported admissions may have actually occurred prior to the period referred to in the questionnaire. Further analysis is planned to investigate other possible explanations for the observed discrepancies. Results for accident and emergency attendances and admissions for road traffic accidents reported by the young people will also be presented.
Discussion & Conclusions We found that the specificities and negative predictive values of parent-reported hospital admissions were high at all ages. The sensitivities and positive predictive values were lower. There are several possible explanations for this. A proportion of respondents may have interpreted the questions about admission to hospital as including visits to A&E and/or outpatient appointments. The HES database only includes A&E data from April 2007 (when the ALSPAC children were aged 15-16 years old) and outpatient data from April 2003 (when they were 11-12) so it was not possible to examine whether this explained the low sensitivities. Further, some hospital admissions would be to non-NHS providers which are not recorded in HES. Conclusions will be drawn when the additional analyses outlined above have been carried out.
Abstract no. 663 Genealogical information from co-insurance networks in pseudonymized administrative claims data in Austria
Florian Endel, Vienna University of Technology, Vienna
Introduction Routinely collected administrative claims data from the Austrian health and social insurance system is available for research in the GAP-DRG database. It is operated by Vienna University of Technology on behalf of the Main Association of Austrian Social Security Institutions. GAP-DRG holds pseudonymized information on reimbursement of prescriptions, inpatient and ambulatory outpatients contacts of almost all 8 million inhabitants. Genealogical information and family relationships are not directly available in the database. In this project, it is indirectly deduced, analyzed and integrated into GAP-DRG. This project is part of the K-Project dexhelpp in COMET and funded by BMVIT, BMWGJ and transacted by FFG.
Methods Co-insurance of relatives as spouses, children and close family members is encoded in the reimbursement information of GAP-DRG. These relationships between two persons are used to extract networks representing individuals who are associated with each other by co-insurance. Persons are classified as children, parents, in a relationship or single based on thorough data analysis and applying rules originating from qualitative descriptions of family structures in Austria. Additional data as the direction of the graph, representing the dependence of one partner on another and weights of edges holding information on e.g. difference in age is included. Visualization and common methods from graph theory are utilized to extract more details about data quality, social structure of the insured population and also limitation of the data and applied approach.
Results Depending on quality requirements, there are around 2,000,000 persons in the final dataset on co-insurance. In addition to the estimation of genealogical information, new insights into the database and especially data quality are acquired (e.g. persons older than 120 years could be identified as miscoded children due to their dependence on their parents). Networks of related persons allow in-depth analysis and informative visualizations. New quality issues were identified and missing information on e.g. the socio-economic status could be imputed or corrected. Furthermore, the estimated personal information enables novel research questions and. Due to the stepwise procedures, the implemented approach can be directly adapted to new data or particular projects.
Discussion Although solid and promising results have been obtained, additional analysis and concrete limitation have to be discussed. The quality and interpretation of co-insurance networks might vary over time, region and data source (e.g. social insurance institution). Because relationships are derived from co-insurance, couples not depending on each other directly or indirectly by a common child cannot be detected. As a result, the identification of parents is of a higher quality. External validation, verification of the methodology and its application have to be discussed.
Conclusion Genealogical information and networks of co-insurance can be estimated using administrative data. The presented method is straightforward and flexible but also pointed out limitations of the data collection and its quality. Previous knowledge about GAP-DRG and its general quality and trustworthiness could be verified. Summarizing, the newly acquired information on relationships and the extracted networks of co-insurance are interesting on their own and are expected to are the basis of novel data analysis and research.
Abstract no. 664 Predicting 90-Day hospital readmission risk for chronic obstructive pulmonary disease (COPD) patients using health administrative data from Quebec, Canada
Erin Yiran Liu, Aman Verma, Deepa Jahagirdar, and David L. Buckeridge, McGill Clinical & Health Informatics, Department of Epidemiology and Biostatistics, McGill University, Montreal
Jean Bourbeau, Respiratory Epidemiology and Clinical Research Unit, Research Institute of McGill University Health Centre and McGill University, Montreal, Montreal
Introduction Chronic obstructive pulmonary disease (COPD) affects 10% of the adult population and is the third leading cause of death in the world. The progressive nature of COPD results in frequent hospitalizations, placing a considerable burden on the healthcare system. While some of these health care visits are unavoidable, many readmissions could be prevented if the transition of care from acute to community-based services was improved. This awareness of the importance of transitions-incare has led to interventions such as discharge care bundles, which include enhanced follow-up care and referral to specialized programs. Given the tailored nature and cost of these interventions, it is important to identify those who are more likely to be readmitted so that hospital administrators can target these high-risk patients. The objective was to develop a model for predicting the 90-day readmission risk for COPD patients following hospitalization.
Methods The data source used for this study was the Population Health Record (PopHR), which contains linked health administrative data for a 25% random sample of residents of Montreal, the second largest city in Canada. The system includes linked data on hospitalizations, ambulatory care visits, prescriptions, and physician billings for 1.4 million individuals. COPD patients were identified using the validated case definition of ≥1 ambulatory claims and/or ≥1 hospitalizations for COPD. Using logistic regression, the risk of 90-day readmission was predicted based on patient characteristics, previous healthcare use and data from the index hospitalization. Due to the high dimensionality of certain data elements, the lasso technique was used as a feature selection tool and model accuracy was assessed using cross-validation on a 20% sample.
Results From Apr 1, 2006 – Mar 31, 2014 there were 12,314 COPD patients who were alive at discharge from their first hospitalization. During the 90-day follow-up, 860 (6.98%) patients died and 2,335 (18.96%) had an urgent readmission. Significant predictors for readmission included age (OR = 1.56, 95% CI: 1.24 - 1.95) for those older than 71 compared to patients ≤ 50 years old, ≥1 ED visit in the previous 6 months (OR = 1.70, 95% CI: 1.44 - 2.01), a respirologist visit in the previous 6 months (OR = 1.32, 95% CI: 1.07 - 1.63), a length of stay of ≥ 6 days on the index hospitalization (OR = 1.51, 95% CI: 1.27 - 1.80), and having a discharge diagnosis of atrial fibrillation (OR=1.58, 95% CI: 1.14 - 2.19), lung cancer (OR = 1.82, 95% CI: 1.41 - 2.36), pneumonia (OR = 2.40, 95% CI: 1.33 - 4.33), or heart failure (OR: 1.72, 95% CI: 1.36 - 2.18). The model produced an area under the curve of 65%.
Conclusions Older patients with a history of ED visits and respirologist consultations and admitted for lung cancer, pneumonia and cardiovascular disorders were more likely to be readmitted. These findings are consistent with previous research that readmissions tend to occur in patients with more severe disease. Further research is needed to assess potential differences in quality of care for readmitted and non-readmitted COPD patients, post-discharge.
Abstract no. 671 Governance of shared health information in Canada
Karim Keshavjee and Linying Dong, University of Toronto, Toronto
Susan Anderson, Orion Health, Edmonton
Diane Edlund and Carol Brien, COACH, Toronto
Selena Davis, University of Victoria, Victoria
Introduction Health organizations in socialized medicine contexts have a unique constraint: they don’t have access to information beyond the boundaries of their own organization. Yet, it is increasingly evident that lowering costs in health care, coordinating care and personalizing care will require the pooling of data from multiple sources.1,2 Other areas of human endeavour have achieved this. From sharing common lands3 to sharing data for transport,4 there are good examples of ways we can share information assets in common.
Methods A literature search of peer-reviewed articles was conducted using Google Scholar and Pubmed on governance of shared health information. A search of the grey literature on governance of shared assets was also conducted in Google. Approximately 100 relevant articles were identified.
Results
The researchers identified over 25 principles relevant to the governance of shared health information. All 25 principles fell into one of four themes: Policy, People, Process or Technology. Through refinement, seven principles were distilled for governing how health information should be shared between multiple health sector entities to achieve system level goals (Table 1).
Discussion To our knowledge, this is the first articulation of principles for using health information as “a resource for action and decision-making” as opposed to “as a resource for documentation and record-keeping” which characterizes the data and IT governance literature. These principles are specific to sharing health information across multiple organizations as well as directly with patients. The principles need to be validated by stakeholders who stand to gain or lose from implementing them in actual practice.
References
Blumenthal D, Chernof B, Fulmer T, Lumpkin J, Selberg J. Caring for High-Need, High-Cost Patients - An Urgent Priority. N Engl J Med. 2016 Sep 8375(10):909-11.
Duarte TT, Spencer CT. Personalized Proteomics: The Future of Precision Medicine. Proteomes. 20164(4). pii: 29.
Ostrom E. Governing the commons. Cambridge university press 2015 Sep 23.
‘Big Data Versus Big Congestion: Using Information To Improve Transport’. McKinsey & Company. N. p., 2015. Web. 25 Nov. 2016.
Abstract no. 672 Health data visualization for consumer/ patient, clinician, and researcher insights
Suzanne Bakken, Adriana Arcia, Dawn Dowding, and Jacqueline Merrill, Columbia University, New York, NY
Sunmoo Yoon, Visiting Nurse Service of New York, New York, NY
Introduction Visualization is an integral component of data science. Visualizations of health data can be an effective means of deriving insights for patients, clinicians and researchers. Information visualization is an important aspect of the Precision in Symptom Self-Management (PriSSM) Center and our efforts range from infographics representing small data, such as blood pressure, physical activity levels, and self-reported anxiety levels designed for consumers/patients, (Figures A & B) to electronic dashboards for clinicians, and network diagrams of big data for research discovery of patterns and potential intervention targets (Figures C & D).
Methods Specific methods vary depending upon of the purpose of the visualization and the intended audience. However, a typical design process begins with prototyping by PriSSM Center Visualization Design Studio experts followed by iterative participatory design with members of the end-user population. For visualizations designed for consumers/patients, formal comprehension testing is also undertaken. A variety of software products are used to create the visualizations. These include Tableau, Adobe Illustrator, Node XL, NVivo, ORA, and custom software that our team built in a previous research project: EnTICE3 (Electronic Tailored Infographics for Community Engagement, Education, and Empowerment) system.
Results
Discussion Visualizations are integral to data science and critical to advanced data analytics. Our team applies a structured approach and a variety of methods to create visualizations which have resulted in designs that allow consumers/patients, clinicians, and researchers to derive insights from data.
Conclusions We continue to evolve our visualization design research as an approach for promoting consumer/patient, clinician, and researcher insights.