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.
Age range
18 Years – 100 Years
Sex
ALL
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Generated to help you prepare — always confirm anything about your own eligibility and care with the study team and your doctor.
The trial coordinator is the person who runs the study day to day. These cover the practical side — logistics, costs, and what taking part would actually mean for your life. The study team confirms whether you meet the criteria; these are questions to ask, not a sign you qualify.
A starting point for the conversation — always confirm anything about your own eligibility, costs, and care with the study team and your doctor.
The proportion of scheduled patient visits kept by the patient (1-day)
Timeframe: The study has 6 waves (or wedges in the stepped-wedge design). The proportion will be measured weekly for the 4 weeks preceding the first wave of CDSS implementation, and then weekly until 8 weeks after the date of the final wave of CDSS implementation.
The proportion of scheduled patient visits kept by the patient (7-day)
Timeframe: The study has 6 waves (or wedges in the stepped-wedge design). The proportion will be measured weekly for the 4 weeks preceding the first wave of CDSS implementation, and then weekly until 8 weeks after the date of the final wave of CDSS implementation.
The proportion of patients with suppressed VL among those with measured VL
Timeframe: The study has 6 waves (or wedges in the stepped-wedge design). The proportion will be measured for the month preceding the first wave of CDSS implementation, and then monthly until 2 months after the date of the final wave of CDSS implementation.
The proportion of patients with suppressed VL among those with scheduled VL measurement, whether or not that measure was taken.
Timeframe: The study has 6 waves (or wedges in the stepped-wedge design). The proportion will be measured for the month preceding the first wave of CDSS implementation, and then monthly until 2 months after the date of the final wave of CDSS implementation.