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

Abstract

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.