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AI predicts missed HIV appointments: could save lives?

NCT ID NCT06604663

Knowledge-focused Sponsor: Brown University Source: ClinicalTrials.gov ↗

What the study statuses mean

This study's is highlighted.

Recruitment status, easiest to join first

Recruiting now
This trial is taking on new participants right now.
Not yet recruiting
Registered, but not yet taking participants.
By invitation only This study
Not open to general applications. Only people the study team invites can take part.
Paused
Paused for now. It may or may not start again.
Ongoing
Running, but no longer taking on new participants.
Completed
The trial has finished. Results may not be published yet.
Stopped early
Stopped early, before it reached the end. That can be for many reasons, including safety.
Cancelled
Cancelled before anyone took part.

Expanded access (not trials)

Expanded access
Not a trial. This treatment can be requested outside a study, case by case, for people who qualify.
Expanded access (paused)
Not a trial. The treatment can normally be requested outside a study, but is unavailable right now.
Expanded access (ended)
Not a trial. The treatment could once be requested outside a study, but no longer can.
Approved
The treatment has been approved, so it is available normally rather than through this programme.

When the status isn't known

Details not published
The full record has not been published yet, so there is little to show here.
Status unknown
This status has not been confirmed recently, so it may be out of date.

First seen Jun 27, 2026 · Last updated Jun 27, 2026

Summary

This study tests whether machine learning can predict which HIV patients are likely to miss clinic visits or have treatment failure. Researchers will give clinic workers risk alerts to help them reach out to high-risk patients. The goal is to improve retention in care and viral suppression across 80,000 patients in Kenya.

What this could mean

Our plain-language read of the trial. This is informational only, not medical advice or a prediction.

Active substance
Clinical decision support system (CDSS) using machine learning algorithms
What this could lead to
If successful, this could help clinics in Africa identify patients at risk of missing appointments or failing treatment, improving HIV care and reducing viral spread.
What could go wrong
This is an observational study without a new drug or treatment. The algorithms may not work as well in real-world settings, and results may not apply to other regions.

This is an AI summary of the original study and may miss details. Read our disclaimer.

Study facts

What this study's own registry entry says, in plain language.

Phase

Not a phased trial

Phase numbers describe drug development. The registry uses this when they do not apply, as it does for trials of devices, procedures or behaviour changes, and for observational studies.

Participants

About 80,000 people

The number the study aims to enrol. It can still change while the study runs.

Started

May 2024

Expected to finish

Oct 2026

An estimate. End dates often move.

Lead sponsor

Other sponsor

The registry's catch-all category, for sponsors it does not file as a company, a government agency, or a research network.

Who can take part

This study's own entry requirements. Only the study team can say for certain whether you qualify.

Ages

18 to 100 years

Sex

Anyone

Healthy volunteers

Not accepted

This study is not open to healthy volunteers. The entry requirements below say who it is open to.

Show the full entry requirements

Copied word for word from the study's registry entry, so the wording is the study team's rather than ours.

Inclusion Criteria: * The study will include adult patients (age 18 and over) receiving HIV care through the AMPATH program in Eldoret, Kenya. There is not a patient-level enrollment process. The primary endpoints will be summarized at the clinic level (e.g., proportion of patients who keep an appointment within 7 days of the scheduled appointment). Exclusion Criteria: * None

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Conditions

The condition(s) this trial relates to.

AIDS HIV infectious disease No-Show Patients Patient Compliance Patient Dropouts Patient Participation Treatment Adherence and Compliance

As listed by the trial registrant

The condition terms exactly as the trial's registrant entered them.

HIV viremia Human immunodeficiency virus Patient dropouts Patient engagement Patient no show Treatment adherence Treatment compliance

How to take part

Only the study team decides who joins. These are the ways to reach them.

  1. The places running it

    1 site. The list below names each one and where it is.

  2. The official record

    The full official record for this study. This one lists no contact details, but it is the first place any would appear.

    Open the record ↗

  3. A doctor treating you

    A doctor who knows your case can contact a study site on your behalf, and can tell you whether this study is worth pursuing at all.

Contacts and locations

Locations

  • AMPATH

    Eldoret, Kenya

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