AI Predictive Adherence Counseling (South Africa)

Palindrome and Right to Care, South Africa (September 2021)

Countries: South Africa

Intervention 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

DUC Meeting Recording

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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