Harnessing Data Science to Improve HIV Care Continuum Outcomes: A Hybrid Type 2 Trial Evaluating a Machine-Learning Algorithm-Based Implementation Strategy - 7. PROJECT SUMMARY Innovations in treatment and care for people with HIV (PWH) have had tremendous positive impacts on health, longevity and quality of life, but a significant percentage of PWH continue to fall through the cracks of our care system. 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. The specific aims of this project are to: (1) Examine the effectiveness of an algorithm-driven CCM/CC implementation strategy on improving HIV outcomes among a population of highly vulnerable PWH. Under this aim, we will conduct an RCT of 2600 PWH nested within 30 care management agencies (15 intervention/15 control; stratified by caseload and viral suppression rates). Primary outcomes are ED visits, hospitalization and viral suppression. (2) Determine the feasibility of an algorithm-driven CCM/CC implementation strategy and identify barriers and facilitators to its successful adoption. Under this aim, we will examine enactment of the algorithm-driven implementation strategy, collecting data on RE-AIM outcomes (reach, adoption, implementation and maintenance) as well as barriers/facilitators to roll-out. (3) Identify key correlates of the success (and limitations) of the implementation strategy at the patient, provider-, and systems-levels. Under this aim, we will identify for whom and through what mechanisms the implementation strategy best achieves positive outcomes and identify specific strategies for improving the implementation strategy and intervention.
