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

Hunter College of City University of New York

Status: Invitation-only

Conditions: HIV (Human Immunodeficiency Virus)

Treatments: Other: Standard of care; Other: predictive emergency room alerts (pERA)

Study type: Interventional

Funder types: Other

Identifiers

NCT07279376

R01MH136903 (U.S. NIH Grant/Contract)

Details and patient eligibility

About

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.

Full 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

Enrollment: 2,600 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Participants must be members of one of the Care Management Agencies that comprise the Community Care Management Partners (CCMP) Health Home
• Participants must be living with HIV

Exclusion criteria

• None, other than those listed above.

Trial design

Primary purpose: Treatment

Allocation: Randomized

Interventional model: Crossover Assignment

Masking: None (Open label)

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2,600 participants in 2 patient groups

Predictive Emergency Room Alerts (perA)Implementation Strategy

Active Comparator group

Description:

Refers to patients within Care Management Agencies that have been randomized to use the pERA implementation strategy to delivery CCM/CC during that study period.

Treatment:

Other: predictive emergency room alerts (pERA)

Standard of Care Implementation Strategy

Other group

Description:

Refers to patients in Care Management Agencies that have been randomized to use their standard of care implementation strategy to deliver CCM/CC during that study period.

Treatment:

Other: Standard of care