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INNOVATION OR EVIDENCE? BOTH!
Evidence-based thinking is sometimes misconceived as a barrier to innovation, when in fact both are vital.1,2 Merlo et al.3 demonstrate the synergy of evidence and innovation, showing how a mobile application for children with attention-deficit hyperactivity disorder supports evidence-based practice. The app enables systematic collection of behavioural observation data from people in the child’s ‘network’. Clinicians analyse this data to formulate intervention strategies and evaluate their effectiveness. Their results from the app show improved collaboration between clinicians, teachers and family members and a positive association with modified behaviours.
ELECTRONIC HEALTH RECORDS: IMPLEMENTING, OPTIMISING AND TRUSTING
Several aspects of electronic health records (EHRs) are discussed in this issue. Priestman et al.4 offer an evidence synthesis of EHR implementation barriers, success factors and operational impacts. Moon et al.5 report a qualitative study of EHR optimisation in a set of US hospitals known for their advanced usage of health information technology, emphasising that in many respects the work only starts after go-live and that dedicated resources are needed to drive the optimisation of workflow, processes and practice. Millares Martin6 raises the question of how far we can trust EHRs, using the simple method of assessing the reliability of smoking status recording in primary care systems.
CONCEPTS AND PRIORITIES FOR LEARNING HEALTH SYSTEMS
McLachlan et al.7 introduce the Heimdall framework as a unifying nomenclature for learning health systems. The authors contend that most work on learning health systems is not identified as such and that the lack of an agreed taxonomy hinders collaboration and progress in the field. This is an important contribution towards acquiring a common language that will improve knowledge sharing and convergence of research in learning health systems.
Another barrier to building learning health systems is the gulf between the worlds of ‘Big Data’ and ‘Digital Health’.8 The limitations of routinely collected data for research purposes are not always recognised.9 Debate on this topic at a workshop held at Medical Informatics Europe 2017 provides the basis for our leading article. We expand that discussion and propose that in fact clinical informatics comprises a greater part of a learning health system than the high profile data science aspects.10
Learning health systems also need digital leaders. With the recent formation of the UK Faculty of Clinical Informatics and the Federation of Informatics Professionals,11 the importance of professional qualifications and leadership has an increasing profile for practitioners in our discipline. Sridharan et al.12 review the evolution of the roles of hospital Chief Information Officer and Chief Clinical Information Officer and discuss the emergence of a new senior role in academic institutions: Chief Research Information Officer.
DATA SETS IN CONTEXT TO INFORM PROGNOSIS
Atashi et al.13 report work in Iran to develop a core data set for intensive care patients, specifically designed to form the basis of a prognostic model that fits a developing country. Again, their work brings both evidence (the literature review that informed their project) and innovation (the expert consultation that took into account the developing country variance from models based on data from developed nation contexts). We look forward to seeing the empirical findings of their model in future publications.
ROBOTS FROM THE FUTURE
In our last issue, we published a fascinating report on robot ward rounds in surgery.14 Our current editorial reflects on that paper, discusses the prospect of wider adoption and notes both limitations and opportunities to enrich clinical utility of the robotic ward round experience.15
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