A Governance Framework for Medical Code Standardization to Enhance Multi-Institutional Data Quality | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Governance Framework for Medical Code Standardization to Enhance Multi-Institutional Data Quality Takanori Yamashita, Shin-ichi Shibata, Atsushi Takada, Taeko Hotta, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7955181/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Feb, 2026 Read the published version in BMC Medical Informatics and Decision Making → Version 1 posted 10 You are reading this latest preprint version Abstract Background: Data quality management is crucial for performing integrated analyses of medical data across multiple institutions, and mapping facility-specific local codes to standardized codes is a critical component of this process. This study aimed to improve the medical data quality of Medical Information Database Network (MID-NET®)-cooperating institutions by developing and implementing a governance framework for medical code standardization. Methods: A governance center was established at Kyushu University Hospital, which developed a differential output tool for detecting real-time change logs in local and standardized codes. This tool was introduced to 18 MID-NET institutions to extract differences between updates and securely transfer them to the governance center. The governance procedures involved collecting and verifying mapping tables, assigning standard codes (HOT, JLAC-10, or ICD-10), and distributing updates to cooperating institutions. The full-scale operation of the governance process began in July 2020, facilitating continuous improvement in mapping accuracy and efficiency. The most optimal standardized code was proposed by the medical profession, and feedback was provided monthly to each institution. Results: After approximately 1.5 years of governance, the correct standardized code assignment rates across all cooperating institutions were 36% for drugs, 29% for laboratory tests, and 67% for diseases. Despite the monthly proposals provided by the governance center, the increase in registrations remained modest, particularly for laboratory tests, where the JLAC-10 codes were complex, highlighting the difficulty of achieving high coverage. However, the accumulation of differential data allowed for continuous monitoring of registration status and provided insights into problems and solutions at each institution. Mechanisms for semi-automatic registration and expansion of the governance system across multiple institutions and vendors were considered to further improve registration rates. Conclusion: Maintaining high-quality data is crucial for ensuring reliable clinical collaboration and establishing a foundation for the secondary use of real-world data. This governance model provides a practical framework for data-driven projects that integrate centralized repositories with local electronic medical records, not only within MID-NET but also for other clinical research database initiatives. Real-world data Data quality management Code standardization Multi-institutional research Full Text Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.docx Additionalfile2.docx Additionalfile3.docx Cite Share Download PDF Status: Published Journal Publication published 25 Feb, 2026 Read the published version in BMC Medical Informatics and Decision Making → Version 1 posted Editorial decision: Revision requested 09 Dec, 2025 Reviews received at journal 30 Nov, 2025 Reviewers agreed at journal 20 Nov, 2025 Reviews received at journal 17 Nov, 2025 Reviewers agreed at journal 11 Nov, 2025 Reviewers agreed at journal 03 Nov, 2025 Reviewers invited by journal 03 Nov, 2025 Editor assigned by journal 03 Nov, 2025 Submission checks completed at journal 30 Oct, 2025 First submitted to journal 30 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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This study aimed to improve the medical data quality of Medical Information Database Network (MID-NET®)-cooperating institutions by developing and implementing a governance framework for medical code standardization.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods: A governance center was established at Kyushu University Hospital, which developed a differential output tool for detecting real-time change logs in local and standardized codes. This tool was introduced to 18 MID-NET institutions to extract differences between updates and securely transfer them to the governance center. The governance procedures involved collecting and verifying mapping tables, assigning standard codes (HOT, JLAC-10, or ICD-10), and distributing updates to cooperating institutions. The full-scale operation of the governance process began in July 2020, facilitating continuous improvement in mapping accuracy and efficiency. The most optimal standardized code was proposed by the medical profession, and feedback was provided monthly to each institution.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults: After approximately 1.5 years of governance, the correct standardized code assignment rates across all cooperating institutions were 36% for drugs, 29% for laboratory tests, and 67% for diseases. Despite the monthly proposals provided by the governance center, the increase in registrations remained modest, particularly for laboratory tests, where the JLAC-10 codes were complex, highlighting the difficulty of achieving high coverage. However, the accumulation of differential data allowed for continuous monitoring of registration status and provided insights into problems and solutions at each institution. Mechanisms for semi-automatic registration and expansion of the governance system across multiple institutions and vendors were considered to further improve registration rates.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusion: Maintaining high-quality data is crucial for ensuring reliable clinical collaboration and establishing a foundation for the secondary use of real-world data. This governance model provides a practical framework for data-driven projects that integrate centralized repositories with local electronic medical records, not only within MID-NET but also for other clinical research database initiatives.\u003c/p\u003e","manuscriptTitle":"A Governance Framework for Medical Code Standardization to Enhance Multi-Institutional Data Quality","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-12 23:13:19","doi":"10.21203/rs.3.rs-7955181/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-09T10:32:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-30T16:50:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"172252129838470243846228346440630577835","date":"2025-11-20T16:24:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-17T12:49:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"240646019056962992978730075515109986092","date":"2025-11-11T11:59:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"48873922805438553976185816626308691503","date":"2025-11-03T15:07:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-03T08:07:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-03T07:33:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-31T03:57:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Informatics and Decision Making","date":"2025-10-31T03:56:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"
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