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Data Use Community
  • HIV Treatment Continuity Technology Intervention Framework (TIF)
    • Outside the Visit
      • Pre Appointment Support Interventions
        • QI-PM Pre Appointment Support
        • Pre-Appointment Reminders (Nigeria)
        • Pre-Appointment Support (South Sudan)
      • Population-Based Scheduling Interventions
        • CMIS Pre Appointment Support & Population Based Scheduling (Eswatini)
      • Congestion Redistribution
        • Lighthouse Trust's Community-based ART Retention and Suppression (CARES) App in Malawi
        • Differentiated Service Delivery Models Support in UgandaEMR
      • Pooling Patient Data
        • Unique Identity (Botswana)
        • Data Analysis and Visualizations (Tanzania)
      • Anticipatory Guidance
    • During the Visit
      • Proactive Adherence Counselling Interventions
        • Missed Appointments Lists (Haiti)
        • AI Predictive Adherence Counseling (South Africa)
        • Machine Learning to Predict Interruption in Treatment (Mozambique)
        • Predictive model for Interruption in Treatment in Patient Treatment Response Dashboard (Nigeria)
      • Reactive Adherence Counseling Interventions
        • Reactive Adherence Counseling (Haiti)
        • Adherence Dashboard (Kenya)
      • Visit Management Interventions
        • EMR Visit Management (Uganda)
    • Missed Appointment Interventions
      • Missed Appointment Reminder
        • Two-way Texting Patient reminders and tracking (Zimbabwe)
        • Patient Reminders and Tracking (Kenya)
        • EMR-ART Missed Appointment Reminder (Ethiopia)
        • Person-Centered Public Health for HIV Treatment (PCPH)
        • Missed Appointment Management (Western Kenya)
        • Rwanda Biomedical Center EMR (RBC EMR)
      • Intensive Outreach Intervention
        • Missed Appointments and Intensive Outreach (Kenya)
        • Patient Tracing (Ethiopia)
        • Identification of Missed Appointments (Malawi)
        • Missed Appointments and Intensive Outreach (Nigeria)
      • Targeted Adherence Support Interventions
        • Enhancing HIV Treatment Continuity: Innovations and Data Use in Kenya's Health Information Systems
  • Patient Identity Management Toolkit
    • Modules
      • Key Considerations in Matching
        • Background
        • Phase 1 - Planning and Analysis
        • Phase 2 - Implementation
        • Phase 3 - Review and Refine
        • Frequently Asked Questions (FAQ)
      • Matching with Biometrics
        • Overview
        • Role in Identity Management
        • Choosing Biometric Characteristics and Modalities
          • Reviewing Studies and Comparisons
          • Reviewing Standards and Guidelines
          • Additional Topics to Consider
        • Trends and Developments
          • Current Trends
          • Future Developments
        • Closing
        • References
        • Glossary
    • Learn from Others
      • Map of Country Implementations
      • Reaching Health Standards and Creating Client Registry in Haiti (2021)
      • Introduction to Biometrics for Patient Identity, Presented by Simprints (2022)
      • Utilizing Biometrics for Unique Patient Identification (UPID) in Côte d’Ivoire (2022)
      • Establishing a Unique Patient Identification (UPI) Framework in Kenya (2023)
      • Malawi Master Patient Index (2023)
      • Piloting a Patient Identity Management System (PIMS) in Haiti (2023)
      • Leveraging Biometrics to Scale a Patient Identity Management System (PIMS) in Nigeria (2023)
      • Leveraging Adaptive Machine Learning Algorithms for Patient Identification in Zimbabwe (2023)
      • OpenHIE23 Meeting in Malawi. Patient Identity Management Collaborative Hackathon. (2023)
      • Strengthening Patient Identity Management (PIM) by Integrating a Client Registry in Rwanda (2023)
      • Patient Identity Management Initiatives in Ethiopia (2023)
      • Patient Identity Management Initiatives in Botswana (2024)
    • References
  • How to Provide Feedback and Input on the TIF and Toolkit
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On this page
  • Countries: South Africa
  • Intervention Description
  • Intervention Details
  1. HIV Treatment Continuity Technology Intervention Framework (TIF)
  2. During the Visit
  3. Proactive Adherence Counselling Interventions

AI Predictive Adherence Counseling (South Africa)

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

PreviousMissed Appointments Lists (Haiti)NextMachine Learning to Predict Interruption in Treatment (Mozambique)

Last updated 8 months ago

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

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

DUC Meeting Recording