Evidence map›Paper›PMID 41191912›Full record

ArticleJMIR medical informatics2025

Attitudes Toward Common Data Models Among Chinese Biomedical Professionals: Cross-Sectional Survey.

Yexian Yu, Yongqi Zheng, Meng Zhang, Junqing Xie, Seng Chan You, Mengling Feng, Siyan Zhan, Feng Sun

Abstract read
In one paragraph

Article in JMIR medical informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Yexian YuSchool of Information and Communication Engineering, Hainan University, Haikou, China.ORCID 0009-0006-8967-2250
Yongqi ZhengDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.ORCID 0009-0000-0583-2170
Meng ZhangDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.ORCID 0000-0003-2514-5612
Junqing XieCenter for Statistics in Medicine, NDORMS, The Botnar Research Centre, University of Oxford, Oxford, United Kingdom.ORCID 0000-0002-0040-0042
Seng Chan YouDepartment of Biomedical Systems Informatics, College of Medicine, Yonsei University, Seoul, Republic of Korea.ORCID 0000-0002-5052-6399
Mengling FengSaw Swee Hock School of Public Health, National University Health System, National University of Singapore, Singapore, Singapore.ORCID 0000-0002-5338-6248
Siyan ZhanDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.ORCID 0000-0001-7252-5349
Feng SunDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.ORCID 0000-0003-4334-6805

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn the rapidly evolving landscape of health informatics, adopting a standardized common data model (CDM) is a pivotal strategy for harmonizing data from diverse sources within a cohesive framework. Transitioning regional databases to a CDM is important because it facilitates integration and analysis of vast and varied health datasets. This is particularly relevant in China, where unique demographic and epidemiologic profiles present a rich yet complex data landscape. The significance of this research from the perspective of the Chinese population lies in its potential to bridge gaps among disparate data sources, enabling more comprehensive insights into health trends and outcomes.

objectiveThis study aimed to understand biomedical professionals' and trainees' acceptance of the CDM in medical data management in China and to explore potential advantages and challenges associated with its promotion, implementation, and development in the country.

methodsWe conducted a questionnaire survey using Sojump and distributed it on WeChat to evaluate the Chinese population's acceptance of transitioning from local databases to a standardized CDM. The survey assessed participants' understanding of the CDM and the Observational Medical Outcomes Partnership CDM, as well as their views on the importance of CDM for regional databases in China. Analysis of the survey results revealed the current state, challenges, and trends in CDM application within Chinese health care, providing a foundation for future efforts in data standardization and sharing. The reliability of the questionnaire data was assessed using Cronbach α and Guttman Lambda 6 to determine internal consistency.

resultsOur survey of 418 participants revealed that 41.9% (175/418) were aware of the CDM. Recognition of CDM increased with higher education levels and was notably higher among professionals in contract research organizations and the pharmaceutical industry. Knowledge of CDM was primarily gained through literature and conferences, with formal education less common. Logistic regression analysis indicated that individuals with doctoral degrees, researchers, executives, medical professionals, data engineers, Centers for Disease Control and Prevention staff, and statisticians were more likely to be aware of CDM. Subgroup analyses showed higher awareness among doctoral versus nondoctoral and Beijing-based versus non-Beijing respondents, while perceived necessity was broadly comparable across subgroups. Overall, 94.7% (396/418) of respondents believed CDM integration in China is necessary for standardization and efficiency. Despite 60.7% (254/418) optimism for the Observational Medical Outcomes Partnership as the preferred CDM, challenges such as mapping traditional Chinese medicine or Chinese medical insurance remain.

conclusionsA large proportion of respondents expressed a favorable view of implementing the CDM in regional databases in China, with notable endorsement from the doctoral group and professionals in contract research organizations or pharmaceutical sectors; subgroup differences were concentrated in awareness rather than perceived necessity. Participants suggested enhancing CDM-related education and establishing clear data-sharing regulations to support CDM advancement in China.

Indexed as

Attitude of Health PersonnelHealth PersonnelAdultChinaCross-Sectional StudiesFemaleHumansMaleMiddle AgedSurveys and Questionnairesattitude of health personnelbig datacommon data modelselectronic health recordshealth information exchangemedical informaticssurveys and questionnaires

Identifiers

PMID41191912
PMCPMC12631093

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.