Seamless EMR data access: Integrated governance, digital health and the OMOP-CDM.

Autor:	Hallinan CM; Health and Biomedical Informatics Centre, Research Information Technology Unit (HaBIC R2), Department of General Practice and Primary Care, The University of Melbourne Faculty of Medicine Dentistry and Health Sciences, Melbourne, Victoria, Australia hallinan@unimelb.edu.au dboyle@unimelb.edu.au., Ward R; Health and Biomedical Informatics Centre, Research Information Technology Unit (HaBIC R2), Department of General Practice and Primary Care, The University of Melbourne Faculty of Medicine Dentistry and Health Sciences, Melbourne, Victoria, Australia., Hart GK; School of Computing and Information Systems, Faculty of Engineering and Information Technology, Centre for the Digital Transformation of Health, The University of Melbourne Faculty of Medicine Dentistry and Health Sciences, Melbourne, Victoria, Australia., Sullivan C; Queensland Digital Health Centre (QDHeC), Centre for Health Services Research, The University of Queensland Faculty of Medicine, Woolloongabba, Queensland, Australia., Pratt N; Quality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia, Adelaide, South Australia, Australia., Ng AP; Clinical Haematology Department, The Royal Melbourne Hospital, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.; Sir Peter MacCallum Department of Oncology, The University of Melbourne Faculty of Medicine Dentistry and Health Sciences, Melbourne, Victoria, Australia., Capurro D; School of Computing and Information Systems, Faculty of Engineering and Information Technology, Centre for the Digital Transformation of Health, The University of Melbourne Faculty of Medicine Dentistry and Health Sciences, Melbourne, Victoria, Australia.; Department of General Medicine, The Royal Melbourne Hospital, Parkville, Victoria, Australia., Van Der Vegt A; Queensland Digital Health Centre (QDHeC), Centre for Health Services Research, The University of Queensland Faculty of Medicine, Herston, Queensland, Australia., Liaw ST; School of Population Health, UNSW, Sydney, New South Wales, Australia., Daly O; School of Computing and Information Systems, Faculty of Engineering and Information Technology, Centre for the Digital Transformation of Health, The University of Melbourne Faculty of Medicine Dentistry and Health Sciences, Melbourne, Victoria, Australia., Luxan BG; Centre for Big Data Research in Health (CBDRH), UNSW, Sydney, New South Wales, Australia., Bunker D; Queensland Digital Health Centre (QDHeC), Centre for Health Services Research, The University of Queensland Faculty of Medicine, Herston, Queensland, Australia., Boyle D; Health and Biomedical Informatics Centre, Research Information Technology Unit (HaBIC R2), Department of General Practice and Primary Care, The University of Melbourne Faculty of Medicine Dentistry and Health Sciences, Melbourne, Victoria, Australia hallinan@unimelb.edu.au dboyle@unimelb.edu.au.
Jazyk:	angličtina
Zdroj:	BMJ health & care informatics [BMJ Health Care Inform] 2024 Feb 21; Vol. 31 (1). Date of Electronic Publication: 2024 Feb 21.
DOI:	10.1136/bmjhci-2023-100953
Abstrakt:	Objectives In this overview, we describe theObservational Medical Outcomes Partnership Common Data Model (OMOP-CDM), the established governance processes employed in EMR data repositories, and demonstrate how OMOP transformed data provides a lever for more efficient and secure access to electronic medical record (EMR) data by health service providers and researchers. Methods Through pseudonymisation and common data quality assessments, the OMOP-CDM provides a robust framework for converting complex EMR data into a standardised format. This allows for the creation of shared end-to-end analysis packages without the need for direct data exchange, thereby enhancing data security and privacy. By securely sharing de-identified and aggregated data and conducting analyses across multiple OMOP-converted databases, patient-level data is securely firewalled within its respective local site. Results By simplifying data management processes and governance, and through the promotion of interoperability, the OMOP-CDM supports a wide range of clinical, epidemiological, and translational research projects, as well as health service operational reporting. Discussion Adoption of the OMOP-CDM internationally and locally enables conversion of vast amounts of complex, and heterogeneous EMR data into a standardised structured data model, simplifies governance processes, and facilitates rapid repeatable cross-institution analysis through shared end-to-end analysis packages, without the sharing of data. Conclusion The adoption of the OMOP-CDM has the potential to transform health data analytics by providing a common platform for analysing EMR data across diverse healthcare settings. Competing Interests: Competing interests: None declared. (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)
Databáze:	MEDLINE
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