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This paper is in the following e-collection/theme issue:

Public (e)Health, Digital Epidemiology and Public Health Informatics (2291) Electronic/Mobile Data Capture, Internet-based Survey & Research Methodology (1738) Participatory Medicine & E-Patients (986) mHealth for Data Collection and Research (1720) Big Data (433) Design of Processes and Workflows (286) Electronic Health Records (1718) HIV/AIDS/STI Prevention and Care (1285) Data Science (701) Vulnerable Populations in Health Research (349)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/71388, first published .
Doctor showing a tablet with a patient medical form

The Lifecycle of Electronic Health Record Data in HIV-Related Big Data Studies: Qualitative Study of Bias Instances and Potential Opportunities for Minimization

The Lifecycle of Electronic Health Record Data in HIV-Related Big Data Studies: Qualitative Study of Bias Instances and Potential Opportunities for Minimization

Authors of this article:

Arielle N'Diaye1 Author Orcid Image ;   Shan Qiao1 Author Orcid Image ;   Camryn Garrett1 Author Orcid Image ;   George Khushf2 Author Orcid Image ;   Jiajia Zhang3 Author Orcid Image ;   Xiaoming Li1 Author Orcid Image ;   Bankole Olatosi4 Author Orcid Image
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Journals

  1. Dejene B, Assabie Y, Tadele M, Adnew B, Tsegaye R, Sintayehu Y, Alemu A, Howe R, Girma T, Tegegne T, Abdissa A. Artificial intelligence for predicting antiretroviral therapy outcomes in people living with HIV: A systematic review of predictive models, predictors and clinical readiness. HIV Medicine 2026 View