Harnessing Data Science to Improve HIV Care Continuum Outcomes: A Hybrid Type 2 Trial Evaluating a Machine-Learning Algorithm-Based Implementation Strategy

Comprehensive Care Management and Care Coordination (CCM/CC) is a medical case management intervention with demonstrated effectiveness in reducing ED visits and hospitalization for PWH, and improving both health outcomes (viral load, CD4 count) and retention in care. However, despite CCM/CC's effectiveness, there are persistent challenges to its implementation. This project is based on the scientific premise that the effectiveness of the CCM/CC intervention can be greatly improved by utilizing a data-driven implementation strategy that optimizes timely provision of CCM/CC services to the patients who need it most. Our community-based collaborator, Comprehensive Care Management Partners (CCMP) Health Home, has developed and validated a machine-learning algorithm that can reliably predict which of its PWH patients are most likely to visit the ED in the next two weeks. In this project, we will apply this algorithm as a targeted implementation strategy for CCM/CC, focusing service provision on the PWH who need it most, when they need it most. Our core hypothesis (supported by preliminary studies data) is that this "just-in-time" strategy for implementing a care management intervention will overcome both provider-level barriers to the provision of CCM/CC services and patient-level barriers to the receipt of HIV treatment and care. We will conduct a Hybrid 2 implementation-effectiveness trial, guided by the RE-AIM implementation science framework and the behavioral economics theory of Scarcity to collect rigorous data on the impact of this algorithm-driven implementation strategy on the reach, effectiveness, adoption, implementation and maintenance of the CCM/CC intervention