ArticleApplied clinical informatics2025
Association of an HIV-Prediction Model with Uptake of Preexposure Prophylaxis.
Article in Applied clinical informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Identify Patients at Risk of HIV Using a Clinical Large Language Model from Electronic Health Records.medRxiv : the preprint server for health sciences · 2026Article
- Identify Patients at Risk of HIV Using a Clinical Large Language Model from Electronic Health Records.Proceedings. IEEE International Conference on Healthcare Informatics · 2026Article
- Optimizing HIV Care Engagement: Usability of a mHealth App for Identifying and Retaining Individuals with Nonviral Suppression in Digital Cohort.Applied clinical informatics · 2026Article
- Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Global efforts aimed at ending human immunodeficiency virus (HIV) incidence have adapted and evolved since the turn of the century. The utilization of machine learning incorporated into an electronic health record (EHR) can be refined into prediction models that identify when an individual is at greater HIV infection risk. This can create a novel and innovative approach to identifying patients eligible for preventative therapy.This study's aim was to evaluate the effectiveness of an HIV prediction model in clinical workflows. Outcomes included preexposure prophylaxis (PrEP) prescriptions generated and the model's ability to identify eligible patients.A prediction model was developed and implemented at the safety-net hospital in Dallas County. Patients seen in primary care clinics were evaluated between July 2020 and June 2022. The prediction model was incorporated into an existing best practice advisory (BPA) used to identify potentially eligible PrEP patients. The prior, basic BPA (bBPA) displayed if a prior sexually transmitted infection was documented, and the enhanced BPA (eBPA) incorporated the HIV prediction model.A total of 3,218 unique patients received the BPA during the study time period, with 2,346 ultimately included for evaluation. There were 678 patients in the bBPA group and 1,666 in the eBPA group. PrEP prescriptions generated increased in the postimplementation group within the 90-day follow-up period (bBPA:1.48 vs. eBPA:3.67 prescriptions per month,
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.