Evidence map›Paper›PMID 41003715›Full record

ArticleJournal of medical systems2025

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

Thepphouthone Sorsavanh, Chang Liu, Goshiro Yamamoto, Yukiko Mori, Shinji Kobayashi, Tomohiro Kuroda

Abstract read
In one paragraph

Article in Journal of medical systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Thepphouthone SorsavanhMedical Informatics Division, Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo Ward, Kyoto, 606-8501, Japan. thepsorsavan@kuhp.kyoto-u.ac.jp.ORCID https://orcid.org/0000-0002-8610-2635
Chang LiuMedical Information Technology and Administration Planning, Kyoto University Hospital, 54 Shogoin Kawaracho, Sakyo Ward, Kyoto, 606-8507, Japan.ORCID https://orcid.org/0000-0002-0515-5226
Goshiro YamamotoPreemptive Medicine and Lifestyle Related Disease Research Center, Kyoto University Hospital, 54 Shogoin Kawaracho, Sakyo Ward, Kyoto, 606-8507, Japan.ORCID https://orcid.org/0000-0002-2014-7195
Yukiko MoriMedical Informatics Division, Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo Ward, Kyoto, 606-8501, Japan.ORCID https://orcid.org/0000-0001-9486-9505
Shinji KobayashiSchool of Medicine, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan.ORCID https://orcid.org/0000-0001-5783-2631
Tomohiro KurodaMedical Informatics Division, Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo Ward, Kyoto, 606-8501, Japan.ORCID https://orcid.org/0000-0003-1472-7203

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Electronic Health RecordsPatient Identification SystemsAlgorithmsChildHumansLanguageLaosAlgorithmsHealth information exchangeLao people’s democratic republicPatient identification systems

Identifiers

PMID41003715
PMCPMC12474658

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.