JMIR Publications

ABSTRACT

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 compromised 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 to 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 five experts from five different medical centers identified local concepts related to anesthesia. The concepts were then matched with standard concepts in the Observational Health Data Sciences and Informatics (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. 169 (32.3%) concepts related to periods and features were added to the OHDSI vocabularies. Eight OMOP CDM tables were implemented with anesthesia data and two new tables (EPISODE and FEATURE) were added to store secondarily computed data. We integrated data from 572609 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.