Testing a computer tool that flags HIV patients at risk of missing care
Tests whether computer predictions help clinics keep adults with HIV in care.
Plain-language title generated from the registry record
Registry title: Data Science and Qualitative Research for Decision Support in the HIV Care Cascade
Key facts
- Where
- Eldoret, KenyaSee the site
Could this be for you?
Answer for yourself. This is not a decision: only the study team can say whether you can take part.
- Are you between 18 and 100 years old?
Source: NCT06604663, who can take part
Where in Kenya
- AMPATHEldoret, KenyaNo longer taking part
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Trial details
- Who runs it
- Brown University
- Trial code
- NCT06604663
- Record updated
- 12 January 2026
- Original description
The goal of this study is to determine whether clinical prediction algorithms derived using statistical machine learning methods can be used to improve patient outcomes in large HIV care programs in sub-Saharan Africa and elsewhere.Read full original descriptionShow less
Data Science for Decision Support in the HIV Care Cascade
The goal of this study is to determine whether clinical prediction algorithms derived using statistical machine learning methods can be used to improve patient outcomes in large HIV care programs in sub-Saharan Africa and elsewhere.
There are two main questions to be answered. First, can the prediction algorithms accurately identify those who are at risk for (a) missing scheduled clinic visits and/or (b) treatment failure, evidenced by elevated HIV viral load? And second, can the risk predictions be used in a structured way to (a) improve retention in care and/or (b) reduce the number of patients having elevated viral load? Researchers will develop machine learning prediction algorithms, incorporate the risk prediction information into the electronic health record, provide guidance to clinical health workers on use of the point-of-care interface tools that display risk prediction information, and incorporate feedback from clinic staff to modify and co-develop the protocol for using risk predictions for improving patient outcomes.
They will then compare the proportion of patients having missed visits and longer-term loss to follow up, and the proportion with elevated viral load, between clinics that use the information from the risk prediction algorithms and those that do not.
Inclusion: 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: None.