Title :

Transforming Anesthesia Data Into the Observational Medical Outcomes Partnership Common Data Model: Development and Usability Study

Author(s) :

Lamer, Antoine [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Abou-Arab, O. [Auteur]
CHU Amiens-Picardie
Bourgeois, A. [Auteur]
Centre Hospitalier Universitaire de Nantes = Nantes University Hospital [CHU Nantes]
Parrot, A. [Auteur]
Popoff, B. [Auteur]
Université de Rouen Normandie [UNIROUEN]
Beuscart, Jean-Baptiste [Auteur]
METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694
Tavernier, Benoit [Auteur]
METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694
Moussa, Mouhamed [Auteur]

Journal title :

Journal of Medical Internet Research

Abbreviated title :

J Med Internet Res

Volume number :

23

Pages :

e29259

Publication date :

2021

ISSN :

1438-8871

English keyword(s) :

data reuse
common data model
Observational Medical Outcomes Partnership
anesthesia
data warehouse
reproducible research

HAL domain(s) :

Sciences du Vivant [q-bio]

English abstract : [en]

Background Electronic health records (EHRs, such as those created by an anesthesia management system) generate a large amount of data that can notably be reused for clinical audits and scientific research. The sharing ...
Show more >Background Electronic health records (EHRs, such as those created by an anesthesia management system) generate a large amount of data that can notably be reused for clinical audits and scientific research. The sharing of these data and tools is generally affected by the lack of system interoperability. To overcome these issues, Observational Health Data Sciences and Informatics (OHDSI) developed the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) to standardize EHR data and promote large-scale observational and longitudinal research. Anesthesia data have not previously been mapped into the OMOP CDM. Objective The primary objective was to transform anesthesia data into the OMOP CDM. The secondary objective was to provide vocabularies, queries, and dashboards that might promote the exploitation and sharing of anesthesia data through the CDM. Methods Using our local anesthesia data warehouse, a group of 5 experts from 5 different medical centers identified local concepts related to anesthesia. The concepts were then matched with standard concepts in the OHDSI vocabularies. We performed structural mapping between the design of our local anesthesia data warehouse and the OMOP CDM tables and fields. To validate the implementation of anesthesia data into the OMOP CDM, we developed a set of queries and dashboards. Results We identified 522 concepts related to anesthesia care. They were classified as demographics, units, measurements, operating room steps, drugs, periods of interest, and features. After semantic mapping, 353 (67.7%) of these anesthesia concepts were mapped to OHDSI concepts. Further, 169 (32.3%) concepts related to periods and features were added to the OHDSI vocabularies. Then, 8 OMOP CDM tables were implemented with anesthesia data and 2 new tables (EPISODE and FEATURE) were added to store secondarily computed data. We integrated data from 5,72,609 operations and provided the code for a set of 8 queries and 4 dashboards related to anesthesia care. Conclusions Generic data concerning demographics, drugs, units, measurements, and operating room steps were already available in OHDSI vocabularies. However, most of the intraoperative concepts (the duration of specific steps, an episode of hypotension, etc) were not present in OHDSI vocabularies. The OMOP mapping provided here enables anesthesia data reuse. Keywords: data reuse, common data model, Observational Medical Outcomes Partnership, anesthesia, data warehouse, reproducible researchShow less >

Language :

Anglais

Audience :

Internationale

Popular science :

Non

Administrative institution(s) :

Université de Lille
CHU Lille

Collections :

• METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694

Submission date :

2023-11-15T05:39:53Z
2024-04-08T12:53:23Z