Evidence map›Paper›PMID 41091728›Full record

ArticlePloS one2025

When access ≠ acceptance: How clinical specialty demands shape mutual recognition on medical examination/test result reuse in Chinese hospitals: A study on a pilot province of China's medical digital reform.

Chao Song, Xinmian Huang, Shasha Qian, Chaoyun Yuan, Shuning Liu, Jun Zhou

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
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.

Chao SongLaboratory Medicine Center, Zhejiang Center for Clinical Laboratories, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China.ORCID https://orcid.org/0000-0002-5917-2224
Xinmian HuangDepartment of Medical Affairs, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China.
Shasha QianDepartment of Medical Affairs, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China.
Chaoyun YuanDepartment of Information Center, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China.
Shuning LiuDepartment of International and Domestic Cooperation, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China.
Jun ZhouDepartment of Laboratory Medicine, Hangzhou Children's Hospital, Hangzhou, Zhejiang, China.ORCID https://orcid.org/0009-0006-2716-6264

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDespite widespread electronic health records adoption, interoperability for sharing examination/test results across healthcare institutions remains limited, leading to redundant testing, increased costs, and compromised care. China's mutual recognition policy for medical examination/test results, implemented via the interoperable results sharing platform(IRSP), aims to address this. However, variations in adoption across clinical specialties and the impact of hospital-level pathway interventions are poorly understood.

methodsUtilizing hospital-level administrative data from Zhejiang Province's IRSP (Oct 2023 - Sep 2024), this quasi-experimental study compared three intervention hospitals (blocking the "Overlook Access" pathway) with three control hospitals. We analyzed core recognition metrics (Access Rate-AR, Total Recognition Rate-TRR, Cross-Hospital Access Rate-CHAR, Cross-Hospital Recognition Rate-CHRR) across 12 clinical specialties. Analyses employed magnitude-based inference for intervention effects, Spearman correlations for specialty variations, and descriptive statistics for hospital-type comparisons.

resultsBlocking the "Overlook Access" pathway significantly increased access metrics (AR: Intervention median 98.9% vs. Control 44.6%, Cohen's d = 2.02; CHAR: 99.5% vs. 57.6%, d = 2.85) but paradoxically decreased TRR (18.2% vs. 44.3%, d = -2.72), with minimal impact on CHRR. Substantial variations existed across specialties: Orthopedics and traditional Chinese medicine showed consistently higher access and recognition, while hepatobiliary and endocrinology faced significant challenges. Pediatrics exhibited high access but critically low recognition (e.g., Hospital H: TRR 2.05%, CHRR 2.82%), attributed to rapid physiological changes and data applicability concerns. Strong correlations existed within access metrics (AR-CHAR, ρ = 0.92, p < 0.001) and within recognition metrics (TRR-CHRR, ρ = 0.88, p < 0.001), but weak correlations between access and recognition.

conclusionThis study reveals a critical distinction between access to external medical records and their actual clinical recognition, demonstrating that information interventions alone are insufficient to improve the recognition rates. Clinical specialty-specific factors significantly influence recognition behaviors, reflecting variations in data utility, stability, and diagnostic practices. Institutional success in promoting mutual recognition depends on comprehensive, multi-level strategies. The IRSP exemplifies China's progress in health data interoperability, yet sustainable mutual recognition ultimately hinges on clinical relevance rather than mere accessibility.

Indexed as

Electronic Health RecordsHospitalsChinaHealth Care ReformHumansPilot Projects

Identifiers

PMID41091728
PMCPMC12527183

What OpenQuestion holds

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LicenceCC BY
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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.