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Enhancing Patient Identification Accuracy in Shared Child Health Records: a Hybrid Approach for the Lao Language

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Hybrid and probabilistic patient matching methods show promise for improving health data exchange in Lao PDR, despite challenges with non-Latin scripts. These approaches help reduce duplicate records in the Shared Child Health Record system.

Keywords:
AlgorithmsHealth information exchangeLao people’s democratic republicPatient identification systems

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Area of Science:

  • Health Informatics
  • Data Science
  • Public Health

Background:

  • The Shared Child Health Record (SCHR) project in Lao PDR aims to improve pediatric healthcare through data exchange.
  • Patient identification and duplicate record challenges are significant due to non-Latin script complexities (phonetic variations, tonal alphabet, temporary names).
  • Existing patient-matching algorithms, designed for Latin scripts, perform poorly in this context.

Purpose of the Study:

  • To assess and adapt patient-matching algorithms for the unique linguistic and infrastructural context of Lao PDR.
  • To evaluate the performance of deterministic, probabilistic, and hybrid matching approaches on a Lao SCHR dataset.

Main Methods:

  • A dataset of 20,433 Lao SCHR records was used.
  • Deterministic, probabilistic (Fellegi-Sunter model with Jaro-Winkler similarity), and hybrid matching methods were assessed.
  • A manual gold standard review of 3,191 matches validated algorithm performance.

Main Results:

  • Hybrid and probabilistic methods outperformed deterministic matching, achieving a 90% recall rate.
  • The hybrid method resolved approximately 2,872 duplicates in the SCHR dataset.
  • Challenges included twin records and temporary-to-permanent name transitions, impacting accuracy.

Conclusions:

  • This study is the first to adapt patient-matching methodologies for Lao PDR's specific context.
  • Hybrid methods show potential for improving health information exchange (HIE) systems in resource-limited settings.
  • Performance gaps persist compared to Latin-based systems, highlighting areas for future development.