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Optimizing Patient Record Linkage in a Master Patient Index Using Machine Learning: Algorithm Development and

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Summary

This study introduces a machine learning tool that automatically optimizes patient record matching algorithms for master patient index (MPI) software. The tool significantly improves the accuracy of linking patient data across different healthcare systems.

Keywords:
Bayesian optimizationFEBRLcomputerizeddata linkageelectronic health recordshealth care systemmachine learningmaster indexmaster patient indexmatching algorithmmedical record linkagemedical record systemsopen-source softwarepilotquality of carerecord link

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Area of Science:

  • Health Informatics
  • Machine Learning Applications
  • Data Management

Background:

  • Modern healthcare requires accurate patient data linkage across multiple sources, often managed by Master Patient Index (MPI) software.
  • Current MPI record linkage relies on manual configuration of automated matching algorithms, demanding specialized expertise.
  • Optimizing these algorithms is crucial for quality patient care and data integrity.

Purpose of the Study:

  • To develop and evaluate a novel machine learning-based software tool for automatic configuration of patient matching algorithms.
  • The tool learns from existing human-linked patient record pairs to optimize algorithm parameters.
  • To enhance the efficiency and accuracy of patient data linkage in healthcare systems.

Main Methods:

  • Developed a free, open-source software tool utilizing Bayesian optimization to tune record linkage algorithm parameters.
  • The tool is designed to be agnostic to specific MPI software, linkage algorithms, and patient populations via a minimal HTTP API.
  • Integrated and validated the tool with SantéMPI, an open-source MPI, using synthetic patient datasets to compare performance against default configurations.

Main Results:

  • Machine learning-optimized configurations achieved over 90% true positive record linkage detection with 100% specificity in all datasets.
  • Compared to baseline methods, the ML-optimized approach significantly increased sensitivity, reaching 100% in some cases.
  • While specificity saw a marginal decrease (95.9%), the overall gains in correctly identifying patient records were substantial.

Conclusions:

  • The developed machine learning software tool effectively enhances the performance of existing patient record linkage algorithms.
  • This optimization can be achieved without requiring in-depth knowledge of the underlying algorithm or specific patient population characteristics.
  • The tool offers a significant advancement in improving data accuracy and quality within healthcare systems.