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
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.
BMJ Health Care Inform. 2024 Feb 21;31(1):e100953. doi: 10.1136/bmjhci-2023-100953.
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. 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. 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. 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. 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.
在本篇概述中,我们将介绍观察性医学结局伙伴关系通用数据模型(OMOP-CDM)、电子病历(EMR)数据存储库中采用的既定治理流程,并展示如何通过 OMOP 转换数据,为医疗服务提供者和研究人员更高效、安全地访问 EMR 数据提供助力。通过假名化和通用数据质量评估,OMOP-CDM 为将复杂的 EMR 数据转换为标准化格式提供了一个强大的框架。这允许创建共享的端到端分析包,而无需直接进行数据交换,从而增强了数据安全性和隐私性。通过安全共享去识别和聚合数据,并在多个经过 OMOP 转换的数据库中进行分析,可以在各自的本地站点内安全地对患者级数据进行防火墙保护。通过简化数据管理流程和治理,并通过促进互操作性,OMOP-CDM 支持广泛的临床、流行病学和转化研究项目,以及医疗服务运营报告。国际和本地范围内采用 OMOP-CDM 可以将大量复杂和异构的 EMR 数据转换为标准化的结构化数据模型,简化治理流程,并通过共享的端到端分析包促进快速可重复的跨机构分析,而无需共享数据。采用 OMOP-CDM 有可能通过为跨不同医疗保健环境分析 EMR 数据提供通用平台,从而改变健康数据分析。