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

The Shared Child Health Record (SCHR) project in Lao People’s Democratic Republic (PDR) aims to enhance pediatric health care services and health outcomes by enabling data exchange between health care systems. However, persistent challenges of duplication due to patient identification are hindered b...

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Bibliographic Details
Published in:Journal of medical systems Vol. 49; no. 1; p. 119
Main Authors: Sorsavanh, Thepphouthone, Liu, Chang, Yamamoto, Goshiro, Mori, Yukiko, Kobayashi, Shinji, Kuroda, Tomohiro
Format: Journal Article
Language:English
Published: New York Springer US 26.09.2025
Springer Nature B.V
Subjects:
Accuracy
Algorithms
Child
Childrens health
Data exchange
Datasets
Electronic Health Records - organization & administration
Electronic Health Records - standards
Health care
Health Informatics
Health Sciences
Health services
Humans
Immunization
Internet access
Language
Laos
Machine learning
Matching
Medicine
Medicine & Public Health
Names
Patient Identification Systems - methods
Patient Identification Systems - standards
Patients
Pediatrics
Phonetics
Probabilistic methods
Quality standards
Statistics for Life Sciences
Laos
Algorithms
Lao people’s democratic republic
Health information exchange
Patient identification systems
ISSN:1573-689X, 0148-5598, 1573-689X
Online Access:Get full text
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Summary:The Shared Child Health Record (SCHR) project in Lao People’s Democratic Republic (PDR) aims to enhance pediatric health care services and health outcomes by enabling data exchange between health care systems. However, persistent challenges of duplication due to patient identification are hindered by non-Latin script complexities, including phonetic variations, a tonal alphabet, and temporary naming practices (e.g., placeholder names such as “Eanoi”). Existing patient-matching algorithms designed for Latin scripts underperform in this context. We assessed deterministic, probabilistic, and hybrid matching approaches using a Lao SCHR dataset of 20,433 records. A manual gold standard review (3,191 matches) validated their performance. Probabilistic matching employed the Fellegi–Sunter model with Jaro‒Winkler similarity, whereas the hybrid method combined deterministic rules (exact name/DOB matches) and probabilistic adjustments for unresolved cases. The hybrid and probabilistic methods consistently outperformed deterministic matching, achieving a 90% recall rate on the SCHR dataset. Despite its lower performance in Lao health records, the hybrid method resolved approximately 2,872 duplicates in SCHR. Challenges included twin records (shared identifiers) and temporary-to-permanent name transitions. This study is the first to adapt patient-matching methodologies for Lao’s linguistic and infrastructural context. While hybrid methods show promise, performance gaps persist compared with those of Latin-based systems. These findings have significant implications with respect to improving the accuracy and efficiency of HIE systems in Lao PDR and other resource-limited settings. Clinical trial number: Not applicable.
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ISSN:1573-689X
0148-5598
1573-689X
DOI:10.1007/s10916-025-02260-6