Brief Report: The Trials of Insurance-Based Data to Care-Rx: Low Enrollment in the Antiretroviral Improvement Among Medicaid EnrolleeS (AIMS) Study

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BACKGROUND: Approaches to strengthen Data-to-Care (D2C) models are needed. We tested an insurance-based D2C prescription model (D2C-Rx), which used antiretroviral therapy (ART) prescription claims to identify people with HIV who fail to fill ART prescriptions in a timely manner and to provide ART adherence support. The Antiretroviral Improvement among Medicaid EnrolleeS (AIMS) study was the first trial of an insurance-based D2C-Rx program. We describe AIMS study enrollment, focusing on patient-participants in the AIMS program arm. SETTING: Virginia, United States. METHODS: AIMS used Virginia Medicaid claims and HIV surveillance data to identify Medicaid enrollees with HIV who were without an ART prescription and their providers; study staff then provided targeted adherence support. The target patient-participant sample size was 1000. The outcome of interest was HIV viral suppression. RESULTS: The study encountered difficulty recruiting patient-participants. Low enrollment prompted major changes to the protocol, including revisions to study design, eligibility criteria, and recruitment methods. These changes were unsuccessful. The trial was terminated because of low patient-participant enrollment (n = 4). CONCLUSIONS: Reasons for low patient-participant enrollment may include phone-based recruitment approach, lengthy identity verification and consent procedures, and lack of participant compensation. Although we aimed to identify people with HIV without a current ART prescription refill, several participants reported having their medications available; this discrepancy suggests issues with unexpected participant behavior (eg, stockpiling) or timeliness and accuracy of prescription claims data. Future insurance-based D2C-Rx interventions should account for these challenges, be tailored to context, and be nimble enough to pivot to alternative recruitment methods when needed.

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