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Published on: September 20, 2018
Implementation and FAIR Evaluation of Clinical Data Provision Framework
Shozo Konishi1, Kazuo Okamura2, Aoi Yamaguchi2
1Department of Medical Informatics, The University of Osaka Graduate School of Medicine.
Studies in Health Technology and Informatics
|May 23, 2026
Summary
A new framework enables secure secondary use of hospital clinical data while protecting patient privacy. This model enhances data sharing for research, achieving high FAIR data maturity except for interoperability.
Area of Science:
- Medical Informatics
- Data Science
- Health Data Governance
Background:
- Growing demand for secondary use of clinical data from industry and academia.
- Need to balance analytical utility with stringent patient privacy requirements.
- Existing frameworks may not adequately address both data utility and privacy concerns.
Purpose of the Study:
- To develop and implement a hospital-based data-provision framework for secure secondary data use.
- To balance analytical utility with patient privacy using anonymized data and hash-based indices.
- To establish a scalable model for FAIR (Findability, Accessibility, Interoperability, Reusability)-oriented data sharing in medical institutions.
Main Methods:
- Developed a lightweight, hospital-based framework with three layers: hospital, PLR platform, and secondary users.
- Implemented anonymized research IDs and hash-based data indices for temporal reconstruction without actual dates.
- Separated the data catalog (research IDs, observation indices, hash-based indices) from the securely stored dataset.
- Utilized the PLR platform for overall coordination and access governance.
- Applied the framework to a perinatal cohort, generating four data categories.
Main Results:
- The framework successfully enabled secure secondary data use for a perinatal cohort.
- FAIR evaluation showed high maturity in Findability, Accessibility, and Reusability.
- Interoperability was limited due to non-standardized, non-machine-readable data formats.
- The framework demonstrated clear data governance and operational scalability.
- Anonymized IDs and hash-based indices facilitated temporal reconstruction while preserving privacy.
Conclusions:
- The developed framework offers a practical and scalable solution for FAIR-oriented secondary use of clinical data.
- It supports secure and efficient data sharing in medical institutions, balancing data utility and patient privacy.
- Addressing data standardization is crucial for improving interoperability in future iterations.
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