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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
  1. Patient Identity Management Toolkit
  2. Modules
  3. Key Considerations in Matching

Phase 3 - Review and Refine

Phase 3: Reviewing algorithm performance and refining as needed

The last phase is the periodic assessment of the matching algorithm to determine if refinements would improve its performance and of the matching process to ensure that it continues to perform effectively and efficiently over time. These reviews will likely need to be more frequent early in the implementation of person marching, such as annually, and become less frequent as the process matures. This review process repeats steps described in Phase 1: Characterize the Data and Determine Matching Algorithm. Including the following areas:

Personnel and communication

  • Ensure the list of responsible personnel is up to date.

  • Ensure the communication channel used for messages is up to date.

Review identifiers and data source limitations (Phase 1, Step 1A and 1B)

  • Determine appropriate identifier combinations

  • Address data limitations

Linkage validation (Phase 1, Step 1B).

  • Perform a quick linkage review.

  • Perform a formal linkage review.

Tuning of matching algorithm (Phase 1, Step 1C).

  • Assess the balance between precision and recall and adjust as needed.

  • Review the run-time of the linkage process.

A final step: celebrate!

As this module has described, designing and implementing a person matching process can seem complicated, involving many steps and many people. However, once the process has been established and potential duplicates adjudicated, take a moment to celebrate reaching this milestone and acknowledge all those who have made it possible. The process described here will help to ensure that each person is uniquely represented within or across systems and datasets and forms the basis for ongoing improvement of the system, future linkages, and potential expansion to additional data sources — all to facilitate improved patient care and accurate population health assessments. Congratulations!

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Last updated 10 months ago