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

BACKGROUND: An 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. MET...

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Autores principales: Yingyong, Thitipong, Aungkulanon, Suchunya, Saithong, Wasun, Jantaramanee, Supiya, Phokhasawad, Kanjanakorn, Fellows, Ian, Naiwatanakul, Thananda, Mobnarin, Jariya, Charoen, Narong, Waikayee, Paiboon, Northbrook, Sanny Chen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9462126/
http://dx.doi.org/10.1136/bmjhci-2022-100601
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author Yingyong, Thitipong
Aungkulanon, Suchunya
Saithong, Wasun
Jantaramanee, Supiya
Phokhasawad, Kanjanakorn
Fellows, Ian
Naiwatanakul, Thananda
Mobnarin, Jariya
Charoen, Narong
Waikayee, Paiboon
Northbrook, Sanny Chen
author_facet Yingyong, Thitipong
Aungkulanon, Suchunya
Saithong, Wasun
Jantaramanee, Supiya
Phokhasawad, Kanjanakorn
Fellows, Ian
Naiwatanakul, Thananda
Mobnarin, Jariya
Charoen, Narong
Waikayee, Paiboon
Northbrook, Sanny Chen
author_sort Yingyong, Thitipong
collection PubMed
description BACKGROUND: An 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. METHODS: The 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. RESULTS: Positive 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. CONCLUSIONS: EMR 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.
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spelling pubmed-94621262022-09-14 Development of automated HIV case reporting system using national electronic medical record in Thailand Yingyong, Thitipong Aungkulanon, Suchunya Saithong, Wasun Jantaramanee, Supiya Phokhasawad, Kanjanakorn Fellows, Ian Naiwatanakul, Thananda Mobnarin, Jariya Charoen, Narong Waikayee, Paiboon Northbrook, Sanny Chen BMJ Health Care Inform Original Research BACKGROUND: An 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. METHODS: The 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. RESULTS: Positive 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. CONCLUSIONS: EMR 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. BMJ Publishing Group 2022-09-08 /pmc/articles/PMC9462126/ http://dx.doi.org/10.1136/bmjhci-2022-100601 Text en © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Original Research
Yingyong, Thitipong
Aungkulanon, Suchunya
Saithong, Wasun
Jantaramanee, Supiya
Phokhasawad, Kanjanakorn
Fellows, Ian
Naiwatanakul, Thananda
Mobnarin, Jariya
Charoen, Narong
Waikayee, Paiboon
Northbrook, Sanny Chen
Development of automated HIV case reporting system using national electronic medical record in Thailand
title Development of automated HIV case reporting system using national electronic medical record in Thailand
title_full Development of automated HIV case reporting system using national electronic medical record in Thailand
title_fullStr Development of automated HIV case reporting system using national electronic medical record in Thailand
title_full_unstemmed Development of automated HIV case reporting system using national electronic medical record in Thailand
title_short Development of automated HIV case reporting system using national electronic medical record in Thailand
title_sort development of automated hiv case reporting system using national electronic medical record in thailand
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9462126/
http://dx.doi.org/10.1136/bmjhci-2022-100601
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