Hostname: page-component-78c5997874-t5tsf Total loading time: 0 Render date: 2024-11-10T06:37:09.839Z Has data issue: false hasContentIssue false
  • English
  • Français

76 Lessons learned during implementation of OMOP common data model across multiple health systems

Part of: JCTS_2024_ABSTRACT_COLLECTION

Published online by Cambridge University Press:  03 April 2024

William Garneau ,
Benjamin Martin ,
Kelly Gebo ,
Paul Nagy ,
Johns Hopkins ,
Danielle Boyce ,
Michael Cook  and
Matthew Robinson
William Garneau
Affiliation:
Biomedical Informatics & Data Science Program, Johns Hopkins University School of Medicine, Baltimore, MD, USA
Benjamin Martin
Affiliation:
Department of Medicine/Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, MD, USA
Kelly Gebo
Affiliation:
Department of Radiology and Radiological Science, Biomedical Informatics & Data Science Program, Johns Hopkins University School of Medicine, Baltimore, MD, USA
Paul Nagy
Affiliation:
Department of Radiology and Radiological Science, Biomedical Informatics & Data Science Program, Johns Hopkins University School of Medicine, Baltimore, MD, USA
Johns Hopkins
Affiliation:
Biomedical Informatics & Data Science Program, Johns Hopkins University School of Medicine, Baltimore, MD, USA
Danielle Boyce
Affiliation:
Department of Medicine/Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, MD, USA
Michael Cook
Affiliation:
Department of Medicine/Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, MD, USA
Matthew Robinson
Affiliation:
Department of Radiology and Radiological Science, Biomedical Informatics & Data Science Program, Johns Hopkins University School of Medicine, Baltimore, MD, USA
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

OBJECTIVES/GOALS: Adoption of the Observational Medical Outcomes Partnership (OMOP) common data model promises to transform large-scale observational health research. However, there are diverse challenges for operationalizing OMOP in terms of interoperability and technical skills among coordinating centers throughout the US. METHODS/STUDY POPULATION: A team from the Critical Path Institute (C-Path) collaborated with the informatics team members at Johns Hopkins to provide technical support to participating sites as part of the Extract, Transform, and Load (ETL) process linking existing concepts to OMOP concepts. Health systems met regularly via teleconference to review challenges and progress in ETL process. Sites were responsible for performing the local ETL process with assistance and securely provisioning de-identified data as part of the CURE ID program. RESULTS/ANTICIPATED RESULTS: More than twenty health systems participated in the CURE ID effort.Laboratory measures, basic demographics, disease diagnoses and problem list were more easily mapped to OMOP concepts by CURE ID partner institutions. Outcomes, social determinants of health, medical devices, and specific treatments were less easily characterized as part of the project. Concepts within the medical record presented very different technical challenges in terms of representation. There is a lack of standardization in OMOP implementation even among centers using the same electronic medical health record. Readiness to adopt OMOP varied across the institutions who participated. Health systems achieved variable level of coverage using OMOP medical concepts as part of the initiative. DISCUSSION/SIGNIFICANCE: Adoption of OMOP involves local stakeholder knowledge and implementation. Variable complexity of health concepts contributed to variable coverage. Documentation and support require extensive time and effort. Open-source software can be technically challenging. Interoperability of secure data systems presents unique problems.

Type
Contemporary Research Challenges
Information
Journal of Clinical and Translational Science , Volume 8 , Issue s1 , April 2024 , pp. 20
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s), 2024. The Association for Clinical and Translational Science