AI Predictive Adherence Counseling (South Africa)
Palindrome and Right to Care, South Africa (September 2021)
Countries: 
South Africa
South AfricaIntervention Description
Model that can predict whether a patient will miss their next scheduled appointment. Patient gets a tailored intervention based on the results.
Presenter: Tom Compton, Right to Care; Kieran Sharpey-Schafer, Palindrome
Intervention Details
Data Elements
Viral load
Demographics
Clinical data (age, gender, viral load history, visit pattern)
Evidence
Compared to adherence scorecard Model performance:
Loss to follow up:
Accuracy – 66%
Specificity- 94%
Sensitivity -61%
Graphs were presented on tracking LTFU at different stages of care
Technology Requirements / Interoperability
Machine learning and AI
Patient risk score are used to assist workers in nudging towards intervention.
Calculations / Algorithms
Machine Learning Algorithm
Total Adherence Score
Factors to Scale
Implementation Considerations
What are the impacts of telling someone they’re high risk?
Governance Considerations
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