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Enrolling by Invitation

NCT Number: NCT07279376

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

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.

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Key information

Conditions

HIV (Human Immunodeficiency Virus) Acquired Immunodeficiency Syndrome Blood-Borne Infections Communicable Diseases Disease Attributes Emergencies Genital Diseases HIV Infections Immune System Diseases Immunologic Deficiency Syndromes Infections Lentivirus Infections Pathologic Processes Pathological Conditions, Signs and Symptoms RNA Virus Infections Retroviridae Infections Sexually Transmitted Diseases Sexually Transmitted Diseases, Viral Slow Virus Diseases Urogenital Diseases Virus Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Community Care Management Partners Health Home

New York, 10016, United States

About this study

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

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

predictive emergency room alerts (pERA)

Other

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.

Standard of care

Other

Care managers interact with patients according to their standard of care protocols

Primary outcomes

  1. ER visits

    Time frame: Each 18 month cluster period (36 months total)

    Number of ER visits made by patients

  2. Hospitalizations

    Time frame: Each 18 month Cluster Period (36 months total)

    Number of days of Hospitalization

  3. Viral Suppression

    Time frame: Each 18 month cluster period (36 months total)

    Number of timepoints at which patient was virally suppressed

  4. CD4 Count

    Time frame: Each 18 month Cluster Period (36 months total)

    CD4 Level at each data collection timepoint

Sponsors and collaborators

Lead sponsor

Hunter College of City University of New York

Other

Registry information

Official study title

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

Important dates

Study start
2025
Primary completion
2029
Study completion
2029
First posted
Dec 12, 2025
Registry last updated
Jan 27, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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