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Transforming Anesthesia Data Into the Observational Medical Outcomes Partnership Common Data Model: Development and
Antoine Lamer1,2,3, Osama Abou-Arab4, Alexandre Bourgeois5
1Univ. Lille, CHU Lille, ULR 2694 - METRICS: Évaluation des technologies de santé et des pratiques médicales, Lille, France.
Journal of Medical Internet Research
|October 29, 2021
Summary
This study successfully mapped anesthesia data into the Observational Medical Outcomes Partnership (OMOP) common data model (CDM). This standardization enables better clinical audit and research using electronic health records (EHRs).
Area of Science:
- Health Informatics
- Clinical Data Standardization
- Anesthesiology Research
Background:
- Electronic health records (EHRs) generate valuable data for research, but interoperability issues hinder data sharing.
- The Observational Medical Outcomes Partnership (OMOP) common data model (CDM) standardizes EHR data for large-scale research.
- Anesthesia data has not been previously mapped into the OMOP CDM, limiting its research potential.
Purpose of the Study:
- To transform anesthesia data into the OMOP CDM.
- To develop supporting vocabularies, queries, and dashboards for anesthesia data exploitation and sharing within the CDM.
Main Methods:
- Local anesthesia data concepts were identified by 5 experts across 5 medical centers.
- Anesthesia concepts were semantically mapped to OHDSI standard concepts.
- Structural mapping was performed between the local data warehouse and OMOP CDM tables; queries and dashboards were developed for validation.
Main Results:
- Of 522 identified anesthesia concepts, 353 (67.7%) were mapped to OHDSI concepts.
- 169 concepts related to periods and features were added to OHDSI vocabularies.
- Anesthesia data from 5,72,609 operations were integrated into 8 OMOP CDM tables, with 2 new tables created; 8 queries and 4 dashboards were provided.
Conclusions:
- While generic anesthesia data existed in OHDSI vocabularies, intraoperative concepts were largely absent.
- The developed OMOP mapping standardizes anesthesia data, enabling its reuse for clinical audits and scientific research.
- This work facilitates large-scale observational studies and longitudinal research in anesthesiology.
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