Development of an anesthesia data warehouse: Preliminary results

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

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.

Development, implementation and preliminary evaluation of clinical dashboards in a department of anesthesia

Clinical dashboards summarize indicators of high-volume patient data in a concise, user-friendly visual format. There are few studies of the use of dashboards to improve professional practice in anesthesiology. The objective of the present study was to describe the user-centered development, implementation and preliminary evaluation of clinical dashboards dealing with anesthesia unit management and quality assessment in a French university medical center. User needs and technical requirements were identified in end user interviews and then synthesized. Several representations were then developed (according to good visualization practice) and submitted to end users for appraisal. Lastly, dashboards were implemented and made accessible for everyday use via the medical center’s network. After a period of use, end user feedback on the dashboard platform was collected as a system usability score (range 0 to 100). Seventeen themes (corresponding to 29 questions and 42 indicators) were identified. After prioritization and feasibility assessment, 10 dashboards were ultimately implemented and deployed. The dashboards variously addressed the unit’s overall activity, compliance with guidelines on intraoperative hemodynamics, ventilation and monitoring, and documentation of the anesthesia procedure. The mean (standard deviation) system usability score was 82.6 (11.5), which corresponded to excellent usability. We developed clinical dashboards for a university medical center’s anesthesia units. The dashboards’ deployment was well received by the center’s anesthesiologists. The dashboards’ impact on activity and practice after several months of use will now have to be assessed.

Methodology to automatically detect abnormal values of vital parameters in anesthesia time-series: Proposal for an adaptable algorithm

Abnormal values of vital parameters such as hypotension or tachycardia may occur during anesthesia and may be detected by analyzing time-series data collected during the procedure by the Anesthesia Information Management System. When crossed with other data from the Hospital Information System, abnormal values of vital parameters have been linked with postoperative morbidity and mortality. However, methods for the automatic detection of these events are poorly documented in the literature and differ between studies, making it difficult to reproduce results. In this paper, we propose a methodology for the automatic detection of abnormal values of vital parameters. This methodology uses an algorithm allowing the configuration of threshold values for any vital parameters as well as the management of missing data. Four examples illustrate the application of the algorithm, after which it is applied to three vital signs (heart rate, SpO2, and mean arterial pressure) to all 2014 anesthetic records at our institution.