NCT07279376

Evaluating an Algorithm-Based Implementation Strategy to Improve HIV Care Outcomes

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

• Status: Enrolling By Invitation
• Phase: N/A
• Study type: Interventional
• Enrollment: 2,600 (estimated)
• Sponsor: Hunter College of City University of New York · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

Summary

This study tests a strategy for helping Care Management Agencies prioritize patients with HIV (PWH) for outreach and support. Under the new strategy, care managers are given a list of highest-priority patients who have been identified by a computer algorithm as being at high risk of going to the emergency room in the next two weeks. This strategy is compared to traditional (standard of care) care management, in which care managers reach out to patients based on a set schedule and their clinical judgement (but not based on a computerized report). We are looking at whether the use of the computer report helps care managers reach the right patients at the right time, preventing them from having to go to the emergency room.

Detailed description

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

Conditions

• HIV (Human Immunodeficiency Virus)

Interventions

Type	Name	Description
OTHER	predictive emergency room alerts (pERA)	pERA is a machine-learning algorithm-driven implementation strategy that identifies patients at higher risk of emergency room visits and alerts the care manager to follow-up with them.
OTHER	Standard of care	Care managers interact with patients according to their standard of care protocols

Timeline

• Start date: 2025-11-18
• Primary completion: 2029-02-01
• Completion: 2029-08-01
• First posted: 2025-12-12
• Last updated: 2026-01-27

Locations

1 site across 1 country: United States