Predictive Analytics + Clinical Decision Support for HIV Prevention

(PrEDICT Trial)

Not currently recruiting at 15 trial locations

Overseen By Julia Marcus, PhD

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Harvard Pilgrim Health Care

Disqualifiers: HIV/STI care, Correctional, Mobile, School-based

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: Unknown

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial aims to determine if special tools in electronic health records, known as Predictive Analytics and Clinical Decision Support, can assist doctors in discussing and prescribing PrEP, a medicine that helps prevent HIV. The focus is on ensuring doctors can easily identify patients who might benefit from PrEP, particularly in community health centers serving individuals with limited healthcare options. Clinics that treated many patients last year and reported new HIV cases are well-suited for this study. The goal is to increase the initiation and continuation of PrEP use, potentially reducing new HIV infections. As an unphased trial, this study provides a unique opportunity to contribute to innovative healthcare solutions that could enhance HIV prevention efforts.

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications.

What prior data suggests that this decision support tool is safe for use in healthcare settings?

Research shows that tools like those used in this trial can help doctors make better decisions by efficiently identifying patients at risk, such as those who might benefit from HIV prevention methods like PrEP.

Previous studies have shown that data from electronic health records (EHRs) can identify patients at higher risk of HIV, enabling doctors to take timely preventive steps. These tools in healthcare have not caused direct harm to patients, as they primarily assist doctors in decision-making.

Doctors who have previously used these tools reported no major issues. They found the tools helpful in identifying patients needing more attention, without causing harm or significant problems. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about using predictive analytics and clinical decision support for HIV prevention because these tools offer a proactive approach to identifying individuals at higher risk of HIV. Unlike standard treatments that involve prescribing PrEP based on general guidelines and patient history, this method uses advanced data analysis to deliver real-time, personalized notifications to healthcare providers. By integrating these insights directly into electronic health records, it empowers doctors to make informed decisions at the point of care, potentially increasing the effectiveness and efficiency of HIV prevention strategies.

What evidence suggests that predictive analytics and clinical decision support are effective for improving PrEP initiation and persistence?

Research has shown that special computer programs can help doctors identify patients who might benefit from HIV prevention medicine, known as PrEP. In this trial, one group of clinics will use predictive analytics and clinical decision support tools to alert healthcare providers about patients who could benefit from PrEP. Studies have found that this method can increase the likelihood of patients starting PrEP, although results have varied. For instance, one study found a small increase in the number of patients starting PrEP care when doctors used these tools. These tools aim to make conversations and prescriptions for PrEP more effective and equitable in community health centers. ^{1 3 5 6 7}

Are You a Good Fit for This Trial?

This trial is for healthcare providers at community health centers that saw over 500 patients in 2024, had at least 10 new HIV diagnoses in 2023, and offer primary care services (excluding pediatrics) active on OCHIN Epic since January 1, 2023.

Inclusion Criteria

My primary care provider is active on OCHIN Epic since 1/1/2023 and does not specialize in pediatrics.

My healthcare facility reported at least 10 new HIV cases in 2023.

My healthcare provider had over 500 patients in 2024.

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Intervention

Providers receive point-of-care notifications via the EHR-embedded decision support tool about patients at increased predicted HIV risk

30 months

Follow-up

Participants are monitored for PrEP initiation and persistence

30 months

What Are the Treatments Tested in This Trial?

Interventions

• Predictive Analytics and Clinical Decision Support

Trial Overview

The study tests a decision support tool integrated into electronic health records. It uses predictive analytics to help providers identify who might benefit from HIV PrEP, discuss it with them, and prescribe it more effectively and equitably.

How Is the Trial Designed?

Group I: Standard of Care Active Control 1 Intervention

Data will be extracted from EHRs of control clinics as a comparison, and those clinics will not actively participate in study activities

Group II: Predictive analytics and clinical decision support Active Control 1 Intervention

Providers in enrolled intervention clinics will receive point-of-care notifications via the EHR-embedded decision support tool about patients who are at increased predicted HIV risk and therefore likely to benefit from PrEP

Who Is Running the Clinical Trial?

Harvard Pilgrim Health Care

Lead Sponsor

Trials

61

Recruited

27,990,000+

Oregon Health and Science University

Collaborator

Trials

1,024

Recruited

7,420,000+

National Institute of Mental Health (NIMH)

Collaborator

Trials

3,007

Recruited

2,852,000+

Oregon Health & Science University (OHSU)

Collaborator

Trials

1

Recruited

20+

OCHIN, Inc.

Collaborator

Trials

24

Recruited

9,964,000+

Published Research Related to This Trial

A predictive model for preventable adverse events (AEs) in hospitalized older patients was developed using data from 6096 patients across two studies, but it was found to be unsatisfactory in accurately predicting these events.

Key risk factors identified included increased age, elective admissions, and admissions to surgical departments, but common factors like comorbidity did not effectively predict preventable AEs.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Can preventable adverse events be predicted among hospitalized older patients? The development and validation of a predictive model. Van De Steeg, L., Langelaan, M., Wagner, C.[2022]

In a study of 1047 patients at a large teaching hospital, 17.7% experienced serious adverse events, with longer hospital stays increasing the likelihood of such events by about 6% for each additional day.

The majority of adverse events were linked to individual errors (37.8%) or interactive causes (15.6%), highlighting the need for healthcare providers to focus on these areas for proactive error prevention.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

An alternative strategy for studying adverse events in medical care. Andrews, LB., Stocking, C., Krizek, T., et al.[2022]

Citations

1.

clinicaltrials.gov

clinicaltrials.gov/study/NCT06805877

Predictive Analytics and Clinical Decision Support to ...

The intervention will include automated EHR notifications to clinicians at 16 OCHIN community health centers about patients who are likely to benefit from PrEP.

2.

pmc.ncbi.nlm.nih.gov

pmc.ncbi.nlm.nih.gov/articles/PMC9731791/

Usability and Utility of Human Immunodeficiency Virus Pre ...

Our findings suggest that an interruptive PrEP CDSS attached to HIV test orders can be an effective tool to increase knowledge and likelihood to initiate PrEP ...

3.

withpower.com

withpower.com/trial/phase-human-immunodeficiency-virus-hiv-infections-2-2025-879b0

Predictive Analytics + Clinical Decision Support for HIV ...

Research shows that predictive analytics can help identify HIV patients at risk of dropping out of care, allowing for timely interventions. A machine learning ...

4.

divisionofresearch.kaiserpermanente.org

divisionofresearch.kaiserpermanente.org/trial-reflects-challenges-of-expanding-hiv-prevention/

Decision support trial reflects challenges of expanding HIV ...

They found a slight but statistically insignificant increase in PrEP care initiation among doctors receiving the email prompts (6% vs. 4.5%).

5.

stacks.cdc.gov

stacks.cdc.gov/view/cdc/112150/cdc_112150_DS1.pdf

Primary Care Providers' Perspectives on Using Automated ...

We and others have previously developed and validated HIV risk prediction models to identify PrEP candidates using electronic health records data. In the.

6.

reporter.nih.gov

reporter.nih.gov/search/Nh1tLw2HPEyPP6IZZnwlzw/project-details/10923913

Project Details - NIH RePORTER

We previously showed that data from electronic health records (EHRs) can be used to identify patients at increased risk of HIV acquisition in two large, general ...

7.

pmc.ncbi.nlm.nih.gov

pmc.ncbi.nlm.nih.gov/articles/PMC8215683/

Machine Learning and Clinical Informatics for Improving ...

Predictive analytic techniques combined with clinical informatics offer the potential for medical providers to intervene in real time to improve HIV care ...