ArticleMethods of information in medicine2021
Optimizing Identification of People Living with HIV from Electronic Medical Records: Computable Phenotype Development and Validation.
Article in Methods of information in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- A topic modeling analysis of stigma dimensions, social, and related behavioral circumstances in clinical notes among patients with HIV.International journal of medical informatics · 2026Article
- Alcohol reduction strategies among persons with hiv: past attempts, self-reported effectiveness, and future strategies of interest.Addiction science & clinical practice · 2025Article
- Development of an electronic health record-based Human Immunodeficiency Virus (HIV) risk prediction model for women, incorporating social determinants of health.BMC public health · 2025Article
- Receipt of long-acting injectable antiretroviral therapy among people with HIV in Southern US states: an assessment using electronic health records and claims data.AIDS research and therapy · 2025Article
- Predicting HIV Diagnosis Among Emerging Adults Using Electronic Health Records and Health Survey Data in All of Us Research Program.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2024Article
- A general framework for developing computable clinical phenotype algorithms.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Using electronic health records for clinical pharmacology research: Challenges and considerations.Clinical and translational science · 2024Review
- Developing a computable phenotype for glioblastoma.Neuro-oncology · 2024Article
- Validation of human immunodeficiency virus diagnosis codes among women enrollees of a U.S. health plan.BMC health services research · 2024Article
- Automated HIV Case Identification from the MIMIC-IV Database.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Demographics, Trends, and Clinical Characteristics of HIV Pre-Exposure Prophylaxis Recipients and People Newly Diagnosed with HIV from Large Electronic Health Records in Florida.AIDS patient care and STDs · 2024Article
- Computational phenotyping with the All of Us Research Program: identifying underrepresented people with HIV or at risk of HIV.JAMIA open · 2023Article
- Develop and validate a computable phenotype for the identification of Alzheimer's disease patients using electronic health record data.Alzheimer's & dementia (Amsterdam, Netherlands)Article
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Authors and funding
8 authors.
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Abstract
backgroundElectronic health record (EHR)-based computable phenotype algorithms allow researchers to efficiently identify a large virtual cohort of Human Immunodeficiency Virus (HIV) patients. Built upon existing algorithms, we refined, improved, and validated an HIV phenotype algorithm using data from the OneFlorida Data Trust, a repository of linked claims data and EHRs from its clinical partners, which provide care to over 15 million patients across all 67 counties in Florida.
methodsOur computable phenotype examined information from multiple EHR domains, including clinical encounters with diagnoses, prescription medications, and laboratory tests. To identify an HIV case, the algorithm requires the patient to have at least one diagnostic code for HIV and meet one of the following criteria: have 1+ positive HIV laboratory, have been prescribed with HIV medications, or have 3+ visits with HIV diagnostic codes. The computable phenotype was validated against a subset of clinical notes.
resultsAmong the 15+ million patients from OneFlorida, we identified 61,313 patients with confirmed HIV diagnosis. Among them, 8.05% met all four inclusion criteria, 69.7% met the 3+ HIV encounters criteria in addition to having HIV diagnostic code, and 8.1% met all criteria except for having positive laboratories. Our algorithm achieved higher sensitivity (98.9%) and comparable specificity (97.6%) relative to existing algorithms (77-83% sensitivity, 86-100% specificity). The mean age of the sample was 42.7 years, 58% male, and about half were Black African American. Patients' average follow-up period (the time between the first and last encounter in the EHRs) was approximately 4.6 years. The median number of all encounters and HIV-related encounters were 79 and 21, respectively.
conclusionBy leveraging EHR data from multiple clinical partners and domains, with a considerably diverse population, our algorithm allows more flexible criteria for identifying patients with incomplete laboratory test results and medication prescribing history compared with prior studies.
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