Development of automated HIV case reporting system using national electronic medical record in Thailand

EMR database

Since 2007, Thailand MoPH has established a national electronic health system to house the central and provincial health data centres (HDC) for management of EMR. All levels of health facilities under the MoPH are required to upload and transfer disaggregated, individualised data to a central database using cloud technology at least once a month. In 2021, the MoPH HDC platform received EMR data (ie, patient demographics, vital signs, test orders, test results, prescriptions, diagnostic codes and healthcare provider details) from 947 MoPH public hospitals, 55 non-MoPH public hospitals and 9760 subdistrict health promotion hospitals. Thai healthcare is dominated by public health facilities accounting for 79% of hospital beds11 and with 21% of total beds in private hospitals. Specialised HIV treatment and care services are mainly provided in public health facilities under the management of universal coverage (UC) to ensure equitable access.

The standard patient-level data collected at each health facility includes demographics and health services.12 Data are subsequently managed and summarised for key performance indicators. The visualised report is accessible for health management and disease control on the web-based HDC dashboard (http://hdcservice.moph.go.th)

Initial algorithm development

HIV infection was identified by applying the surveillance case definition of HIV/AIDS from the 2015 national guidelines for reporting notifiable communicable disease in Thailand.13 In order to maximise the utility and performance characteristics of the algorithm, six separate conditions using complementary approaches to HIV case detection were created. A panel of physicians, including experts in the diagnosis and treatment of HIV infection, provided recommendations for the development of these conditions. The HIV case detection algorithm combined national laboratory testing codes and results, Thai Medicine Terminology prescriptions14 and International Classification of Diseases, 10th Revision (ICD-10) diagnostic codes. A list of laboratory tests, ICD-10 and antiretroviral (ARV) medication can be found in online supplemental appendix 1.

We identified six conditions under which a classification of HIV positive would be a reasonable conclusion and ordered them based on the panel’s likelihood of false positivity. The initial algorithm classified an individual as positive if any one of the six conditions (A, B1, B2, C1, C2 and D1) were met. Condition A is met if laboratory test (anti-HIV, HIVDNA PCR or viral load) identified the individual as HIV infected. For HIV antibody testing, only a ‘positive’ result was defined as positive, whereas ‘negative’ and ‘inconclusive’ results were defined as negative. The presence of detectable viral load was defined as HIV-positive regardless of test result. Conditions B1, B2, C1 and C2 were based on clinical evidence and designed to detect those patients not identified by condition A due to incomplete HIV testing results in EMR. As a result of confidentiality concerns with sharing HIV laboratory results, the laboratory information system not linked to the hospital information system (HIS), and in other cases formatting issues when transferring data from hospital HIS to MoPH HDC prevent HIV laboratory results from being represented in the EMR. Condition B1 was considered met if an individual had three or more HIV-related ICD10 events as well as ARV drug use for at least three visits. Condition B2 was considered satisfied if there were one or two HIV-related ICD10 events and a CD4 test result of <200. C1 is a weaker condition that is satisfied with the presence of any HIV-related ICD10 event in the patient history. C2 and D1 are satisfied based solely on ARV use with C1 triggered when ARVs are reported on two visits >60 days apart and D1 is triggered if they are ≤60 days apart. The initial algorithm distinguishes four levels of confidence in a determination of a positive, ranging from laboratory confirmed to possible clinical evidence. Our algorithm criteria are summarised in table 1.

Assessing validity

We assessed the validity the initial algorithm by applying it to the MoPH EMR from 31 hospitals that were interested in participating from 3 high HIV burden provinces in 2017. HIV positive patients from 31 hospitals were extracted from 460,575 HIV positives cases in the MoPH HDC platform to assess algorithm performance. To verify that subjects were HIV-infected, we used national AIDS programme (NAP) data for ‘true HIV-positive’ and cross-matched with individuals identified through the HIV case detection algorithm. The NAP is National Health Security Office’s electronic database used for recording clinical and laboratory services for HIV monitoring and reimbursement.15 Identification of an HIV-infected patient by the NAP occurs at the hospital, where local healthcare providers register all HIV patients into the NAP for HIV test, CD4, and viral load test reimbursement for all Thai citizens regardless of health insurance scheme. This helps ensure that patients identified by the NAP are truly infected with HIV. Charts of patients who were not identified by NAP were further reviewed by infectious disease specialists from that hospital. The PPV of identifying HIV/AIDS cases was used to measure accuracy of the algorithm. We also reviewed the distribution of subjects by number of diagnostic codes and confirmatory evidence of HIV infection using HIV viral load and ARV therapy results.

Final algorithm development

The expert reviews for the false positives found in the validation step were then analysed to determine whether any refinement to the algorithm conditions could be made. Frequencies were calculated for the expert determined reason for the misclassification and each reason was assessed to investigate whether there were additional constraints to the conditions that could be added to reduce the false positive likelihood. The resulting algorithm was labelled the final algorithm and was applied to the MoPH EMR from 2014 to 2020 to identify HIV-infected subjects for HIV case reporting system.

Automated HIV case reporting system development

The final algorithm was used to build the new HIV case reporting system called Epidemiological Intelligence Information System (EIIS). Data were integrated, stored, managed and analysed in the MoPH HDC cloud-based warehouse. EIIS Data warehouse security measures were strictly enforced to ensure data integrity at all levels. The system limited access to different level of authorised users. The data warehouse was set up to be read-only by default to prevent any threatening from being executed on the data. National identification number was encrypted applying the customised Hash Algorithm. Specifically, only the hashed version of national ID was stored in a database, which was decrypted to only authorised users.

Application of the initial HIV case detection algorithm

We applied the initial algorithm to the MoPH EMR data. All patients who were seen at hospitals under the MoPH HDC platform between 2014 and 2020 were included in this study. The HIV case detection algorithm identified a total of 4 60 575 cases among all patients receiving HIV services in 947 MoPH hospitals, 2243 subdistrict health promotion hospitals and 55 non-MoPH public hospitals. Twelve per cent of the cases were reported to have died. Table 2 shows unduplicated counts of patient who met the case detection criteria for each condition. Individuals were assigned to the highest evidence level for which they qualified. Seventy percent of cases were identified by both diagnosis and prescription (condition B1). Four percent of identified cases had strong evidence of HIV laboratory results (condition A) because HIV laboratory codes were included in the standard 43 files later on in 2017.

Accuracy of the initial algorithm

We identified 26 138 individual patients who met the HIV-infected case detection algorithms at 31 participating hospitals. After matching individual data with NAP, we found 18 647 records (71%) registered in NAP and an additional 7491 patients not identified by NAP. Patient charts of 7491 (100%) were reviewed by infectious disease specialists from their respective hospitals. Of these 7491, 4924 (66%) were correctly classified as HIV positive by our case-detection algorithms, 2120 (28%) were misclassified as HIV-positive (false positive) and 447 (6%) were not found in the hospital database. Positive cases who were not registered in NAP were mainly non-Thai citizens, covered under non-UC health insurance schemes or self-paid patients. PPV of identifying HIV cases based on the algorithm was (18 647+4924)/26 138=90% overall and 98% (16741+3481)/20701 among individuals receiving services in the past year.

Generating a final algorithm

Of the 2120 false positives identified by the review, the experts noted incorrect ICD10 code as the reason in 71% of cases where a reason was given (see table 3). This indicates that condition C1 might reduce the false positivity rate considerably; however, this would not be desirable as it also accounts for a large proportion of the individuals the algorithm classifies as positive (see table 2). As such, we felt that the cost in terms of the introduction of false negatives would be too high.

Postexposure prophylaxis (PEP) and pre-exposure prophylaxis (PrEP) accounted for 28% of false positives where a reason was given and Hepatitis B for an additional 1%. Recognising that (1) some medications used to treat HIV infection are also used for hepatitis B; (2) monotherapy of ARV drugs is not considered an effective HIV medication unless prescribed in combination with an additional drug and (3) dual therapy with emtricitabine and tenofovir are prescribed for PrEP and PEP, we further refined the algorithm to include triple drug regimen for HIV medication to avoid misclassification.

Figure 1 summarises development and validity assessment of the HIV case detection algorithms. The final algorithm replaces conditions B1, C2 and D1 by B1F, C2F and D1F, where the only difference is that the conditions for the final algorithm require triple drug ARV therapy to be triggered. Applying it to the MoPH EMR data found that the final algorithm classified 449 088 individuals as HIV positive, vs 460 575 for the initial algorithm. The similarity of volume of classified cases indicates that the restriction of the conditions to triple therapy was not overly restrictive.

Request permission

The final algorithm experienced a large reduction in the number of hospitals, going from 3245 in the initial algorithm to 1496 in the final. Around 70% of hospitals are subdistrict health promoting hospitals providing primary care and health promotion services, but not ART. Triple drug regimen requirement reduced number of cases from these hospitals. Thus it is unsurprising that most cases in the EMR system should come from a more limited number of hospitals, whereas PrEP and PEP regimens would be present in a wider array of facilities.