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

• Published in: BMJ health & care informatics Vol. 29; no. 1; p. e100601
• Main Authors: Yingyong, Thitipong, Aungkulanon, Suchunya, Saithong, Wasun, Jantaramanee, Supiya, Phokhasawad, Kanjanakorn, Fellows, Ian, Naiwatanakul, Thananda, Mobnarin, Jariya, Charoen, Narong, Waikayee, Paiboon, Northbrook, Sanny Chen
• Format: Journal Article
• Language: English
• Published: London BMJ Publishing Group Ltd 01.09.2022; BMJ Publishing Group LTD; BMJ Publishing Group
• Subjects: Acquired immune deficiency syndrome; AIDS; Algorithms; Antiretroviral drugs; case reporting system; Disease prevention; Documentation; Electronic health records; HIV; HIV case detection algorithm; HIV/AIDS; Human immunodeficiency virus; Laboratories; Medical records; Original Research; Thailand; Thailand
• ISSN: 2632-1009; 2632-1009
• DOI: 10.1136/bmjhci-2022-100601

BackgroundAn electronic medical record (EMR) has the potential to improve completeness and reporting of notifiable diseases. We developed and assessed the validity of an HIV case detection algorithm and deployed the final algorithm in a national automated HIV case reporting system in Thailand.MethodsThe HIV case detection algorithms leveraged a combination of standard laboratory codes, prescriptions and International Classification of Diseases, 10th Revision diagnostic codes to identify potential cases. The initial algorithm was applied to the national EMR from 2014 to June 2020 to identify HIV-infected subjects to build the national HIV case reporting system (Epidemiological Intelligence Information System (EIIS)). A subset of potential positives identified by the initial algorithm were then validated and reviewed by infectious disease specialists. This review identified that a proportion of the false positives were due to pre-exposure prophylaxis/postexposure prophylaxis (PrEP/PEP) antiretrovirals, and so the algorithm was refined into a ‘Final Algorithm’ to address this.ResultsPositive predictive value of identifying HIV cases was 90% overall for the initial algorithm. Individuals misclassified as HIV-positive were HIV-negative patients with incorrect diagnostic codes, prescription records for PrEP, PEP and hepatitis B treatment. Additional revision to the algorithm included triple drug regimen to avoid further misclassification. The final HIV case detection algorithm was applied to national EMR between 2014 and 2020 with 449 088 HIV-infected subjects identified from 1496 hospitals. EIIS was designed by applying the final algorithm to automated extract HIV cases from the national EMR, analysing them and then transmitting the results to the Ministry of Public Health.ConclusionsEMR data can complement traditional provider-based and laboratory-based disease reports. An automated algorithm incorporating laboratory, diagnosis codes and prescriptions have the potential to improve completeness and timeliness of HIV reporting, leading to the implementation of a national HIV case reporting system.