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  • HIV Treatment Continuity Technology Intervention Framework (TIF)
    • Outside the Visit
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      • Reactive Adherence Counseling Interventions
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        • 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 C么te d鈥橧voire (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: Haiti
  • Intervention Description
  • Intervention Details
  1. HIV Treatment Continuity Technology Intervention Framework (TIF)
  2. During the Visit
  3. Reactive Adherence Counseling Interventions

Reactive Adherence Counseling (Haiti)

I-Tech, Haiti (April 2021)

PreviousReactive Adherence Counseling InterventionsNextAdherence Dashboard (Kenya)

Last updated 1 year ago

Countries: Haiti

Intervention Description

Introduces predictive analytics to improve HIV clinical management. It pairs improvements to a routine data system with theory-based behavior change interventions for patients and health care workers.

Presenter: Nancy Puttkammer, I-TECH

Intervention Details

Intervention Attributes
Text

路 Appointment Date (history)

路 Visit Date

路 Proportion of days covered based on dispensing (changed with COVID)

路 Sex

路 Age

路 Marital status

路 Time from HIV diagnosis to enrollment in HIV care and treatment

路 Time from HIV diagnosis to ART initiation

路 Baseline CD4 count

路 Body mass index

路 World Health Organization (WHO) stage of HIV disease progression ART regime.

Puttkammer N, Simoni JM, Sandifer T, et al. An EMR-Based Alert with Brief Provider-Led ART Adherence Counseling: Promising Results of the InfoPlus Adherence Pilot Study Among Haitian Adults with HIV Initiating ART. AIDS Behav 2020. PMID: 32715409

Can work in EHR

Interest in exchange of risk information with community health worker tool/app

Risk level using data elements at left

Need to implement within OpenMRS-based EMR

Prediction model needs refreshing as conditions change (multi-month dispensing)

Most helpful when using EHR at the point-of-care

Will need to be re-implemented with OpenMRS rollout

Threshold of data quality needed (being investigated)

Prediction models are being revisited using machine learning (super learner models) with additional data elements.

Complexity of implementing a clinical decision support prediction model needs to be weighed against the benefit of added sensitivity and specificity gained (is it worth it)

Need governance (what sensitivity and specificity of prediction is adequate; whether it鈥檚 suitable to implement in all facilities using EMR or only sites with sufficient data quality)

Need actionable clinical and psychosocial support for those determined to be at high risk

Data Elements

Evidence

Technology Requirements / Interoperability

Calculations / Algorithms

Factors to Scale

Implementation Considerations

Governance Considerations

DUC Meeting Recording