Division of Infectious Diseases, Beth Israel Deaconess Medical Center, Lowry Medical Office Building Suite GB, 110 Francis Street, Boston, MA, 02215, USA.
University of Massachusetts Medical School, Worcester, MA, USA.
AIDS Behav. 2021 Nov;25(11):3651-3657. doi: 10.1007/s10461-021-03252-6. Epub 2021 Apr 2.
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.
识别有感染艾滋病毒风险的患者可能具有挑战性。初级保健提供者（PCP）可能会受益于有助于识别合适的艾滋病毒暴露前预防（PrEP）候选人的工具。我们和其他人之前已经开发并验证了艾滋病毒风险预测模型，以使用电子健康记录数据来识别 PrEP 候选人。在当前的研究中，我们召集了 PCP 的焦点小组，以了解他们对使用预测模型在临床实践中识别 PrEP 候选人的看法。PCP 愿意使用预测模型来识别 PrEP 候选人。PCP 认为，模型可以促进医患之间关于艾滋病毒风险的沟通，消除和规范艾滋病毒风险评估，帮助患者准确感知自己的风险，并识别可能被遗漏的 PrEP 候选人。然而，PCP 对患者对搜索其医疗记录的反应、潜在的保密性泄露的危害以及模型预测的准确性表示担忧。对 PrEP 临床决策支持的兴趣在经验不足的 PrEP 提供者中最大。预测模型的成功实施需要根据提供者的偏好进行调整，并解决对其使用的担忧。
