HIV (Human Immunodeficiency Virus)NCT07279376
Evaluating an Algorithm-Based Implementation Strategy to Improve HIV Care Outcomes
A study testing predictive emergency room alerts (pERA) in people with HIV (Human Immunodeficiency Virus), run by Hunter College of City University of New York at 1 site in United States.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- HIV (Human Immunodeficiency Virus)
- What is being tested?
- predictive emergency room alerts (pERA)
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 2,600 participants
- Where
- 1 siteUnited States
- Study dates
- 18 Nov 2025to Aug 2029
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- 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
Who cannot join
- None, other than those listed above.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United States1 site
Community Care Management Partners Health Home
New York, New York, 10016
About this study
Official title: Harnessing Data Science to Improve HIV Care Continuum Outcomes: A Hybrid Type 2 Trial Evaluating a Machine-Learning Algorithm-Based Implementation Strategy
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.
Read the 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)
- Being tested
- predictive emergency room alerts (pERA), Standard of care
- Keywords
- HIV, emergency room, implementation science, machine-learning algorithm
- Medical subject headings
- Acquired Immunodeficiency Syndrome, Emergencies
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
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 careOther
Care managers interact with patients according to their standard of care protocols
| Study group | Role | What they receive |
|---|---|---|
| Predictive Emergency Room Alerts (perA)Implementation Strategy | Active comparator | 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. Receives: Other: predictive emergency room alerts (pERA) |
| Standard of Care Implementation Strategy | Other | 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. Receives: Other: Standard of care |
What is being measured
The primary outcome is the main question the study is designed to answer.
ER visits
Number of ER visits made by patients
Measured: Each 18 month cluster period (36 months total)
Hospitalizations
Number of days of Hospitalization
Measured: Each 18 month Cluster Period (36 months total)
Viral Suppression
Number of timepoints at which patient was virally suppressed
Measured: Each 18 month cluster period (36 months total)
CD4 Count
CD4 Level at each data collection timepoint
Measured: Each 18 month Cluster Period (36 months total)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Dec 2025
- Study start18 Nov 2025
- Primary completionFeb 2029 (estimated)
- Study completionAug 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for HIV (Human Immunodeficiency Virus)Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- None (open label)
- Enrolment
- 2,600 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Hunter College of City University of New York
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Sarit Golub, Hunter College of City University of New York
- Investigators
- Sarit A. Golub, PhD, MPH, Principal investigator, Hunter College of The City University of New York
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07279376
- Sponsor's protocol ID
- R01MH136903
- Status last verified
- Jan 2026
- First submitted
- 9 Dec 2025
- Last updated
- 27 Jan 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: State health department requirements regarding these electronic medical record data prohibit sharing IPD.
Study at a glance
From the registry record, last updated by the sponsor on 27 Jan 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Harnessing Data Science to Improve HIV Care Continuum Outcomes: A Hybrid Type 2 Trial Evaluating a Machine-Learning Algorithm-Based Implementation Strategy
- ClinicalTrials.gov ID
- NCT07279376
- Acronym / sponsor ID
- R01MH136903
- Conditions
- HIV (Human Immunodeficiency Virus)
- Keywords
- HIV, emergency room, implementation science, machine-learning algorithm
- Study type
- Interventional
- Recruitment status
- Enrolling by invitation
- Enrolment
- 2,600 participants (estimated)
- Lead sponsor
- Hunter College of City University of New YorkUniversity, hospital or other
- Study start
- 18 Nov 2025 (actual)
- Primary completion
- Feb 2029 (estimated)
- Study completion
- Aug 2029 (estimated)
- First posted
- 12 Dec 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
Study design
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Predictive Emergency Room Alerts (perA)Implementation Strategy | Active comparator | Other: predictive emergency room alerts (pERA)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. |
| Standard of Care Implementation Strategy | Other | Other: Standard of careRefers 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (2 listed)
- 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
Key exclusion criteria (1 listed)
- None, other than those listed above.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| 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 | Predictive Emergency Room Alerts (perA)Implementation Strategy |
| Standard of careCare managers interact with patients according to their standard of care protocols | Other | Standard of Care Implementation Strategy |
Primary outcome
ER visits
Time frame: Each 18 month cluster period (36 months total)
Number of ER visits made by patients
Hospitalizations
Time frame: Each 18 month Cluster Period (36 months total)
Number of days of Hospitalization
Viral Suppression
Time frame: Each 18 month cluster period (36 months total)
Number of timepoints at which patient was virally suppressed
CD4 Count
Time frame: Each 18 month Cluster Period (36 months total)
CD4 Level at each data collection timepoint
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 27 Jan 2026.
- Individual participant dataSharing planned: No. State health department requirements regarding these electronic medical record data prohibit sharing IPD.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Sarit A. Golub, PhD, MPH principal investigator
Hunter College of The City University of New York
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing predictive emergency room alerts (pERA) in people with HIV (Human Immunodeficiency Virus), run by Hunter College of City University of New York at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: enrolling by invitation, as of the sponsor's last update on 27 Jan 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
ER visits (time frame: Each 18 month cluster period (36 months total)). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 27 Jan 2026.