Integrated Data Governance, Digital Health, and the Common Data Model (OMOP-CDM)-Reference-Cited by-同舟云学术

Integrated Data Governance, Digital Health, and the Common Data Model (OMOP-CDM)

Published:2023-10-27 Issue: Volume: Page:
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Author:

Hallinan Christine Mary¹^ORCID,Ward Roger¹^ORCID,Hart Graeme K²^ORCID,Sullivan Clair³^ORCID,Pratt Nicole⁴^ORCID,Ng Ashley P⁵^ORCID,Capurro Daniel²^ORCID,Vegt Anton Van Der³^ORCID,Liaw Teng⁶^ORCID,Daly Oliver²^ORCID,Luxan Blanca Gallego⁷^ORCID,Bunker David³,Boyle Douglas⁸^ORCID

Affiliation:

1. HaBIC Research Information Technology Unit (HaBIC R2), Department of General Practice and Primary Care, Faculty of Medicine, Dentistry & Health Sciences, The University of Melbourne

2. School of Computing and Information Systems, Faculty of Engineering and Information Technology, Centre for the Digital Transformation of Health, Faculty of Medicine, Dentistry, and Health Sciences, The University of Melbourne

3. Queensland Digital Health Centre (QDHeC), Centre for Health Services Research, Faculty of Medicine, The University of Queensland

4. Quality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia

5. Clinical Haematology Department, The Royal Melbourne Hospital and Peter MacCallum Cancer Centre

6. Clinical Informatics & Digital Health, School of Population Health, UNSW, Sydney

7. Centre for Big Data Research in Health (CBDRH), UNSW, Sydney

8. HaBIC Research Information Technology Unit (HaBIC R2), Department of General Practice andPrimary Care, Faculty of Medicine, Dentistry & Health Sciences, The University of Melbourne

Abstract

Abstract Adoption of the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM) internationally and in Australia has enabled the conversion of vast amounts of complex, and heterogeneous electronic medical record (EMR) data into a standardised structured data model. This helps simplify governance processes and facilitates rapid, repeatable cross-institution analysis through shared end-to-end analysis packages without the sharing of raw data. Combined with pseudonymisation and standardised data quality assessments, the OMOP-CDM provides a powerful model to support ethical real-world ‘big’ data research. The continued adoption of OMOP-CDM, ongoing development efforts, and the emphasis on sound governance practices all contribute to the realisation of OMOP’s utility in unlocking valuable EMR data. These factors collectively support a wide range of applications, from health service operational reporting to diverse clinical, epidemiological, and translational research projects.

Funder

Australian Government

Publisher

Research Square Platform LLC

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