Open AccessPrimary Care Providers’ Perspectives on Using Automated HIV Risk Prediction Models to Identify Potential Candidates for Pre-exposure Prophylaxis
Author(s) -
Polly van den Berg,
Victoria Powell,
Ira B. Wilson,
Michael Klompas,
Kenneth H. Mayer,
Douglas Krakower
Publication year - 2021
Publication title -
aids and behavior
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.994
H-Index - 106
eISSN - 1573-3254
pISSN - 1090-7165
DOI - 10.1007/s10461-021-03252-6
Subject(s) - pre exposure prophylaxis , confidentiality , medicine , primary care , health psychology , human immunodeficiency virus (hiv) , predictive modelling , medical record , risk assessment , public health , medline , family medicine , nursing , men who have sex with men , machine learning , computer science , computer security , syphilis , political science , law , radiology
Identifying patients at increased risk for HIV acquisition can be challenging. Primary care providers (PCPs) may benefit from tools that help them identify appropriate candidates for HIV pre-exposure prophylaxis (PrEP). We and others have previously developed and validated HIV risk prediction models to identify PrEP candidates using electronic health records data. In the current study, we convened focus groups with PCPs to elicit their perspectives on using prediction models to identify PrEP candidates in clinical practice. PCPs were receptive to using prediction models to identify PrEP candidates. PCPs believed that models could facilitate patient-provider communication about HIV risk, destigmatize and standardize HIV risk assessments, help patients accurately perceive their risk, and identify PrEP candidates who might otherwise be missed. However, PCPs had concerns about patients' reactions to having their medical records searched, harms from potential breaches in confidentiality, and the accuracy of model predictions. Interest in clinical decision-support for PrEP was greatest among PrEP-inexperienced providers. Successful implementation of prediction models will require tailoring them to providers' preferences and addressing concerns about their use.