Anesthesia provider model, hospital resources, and maternal outcomes.
Ontology highlight
ABSTRACT: Objective
Determine the ability of anesthesia provider model and hospital resources to explain maternal outcome variation.Data source/study setting
1,141,641 obstetrical patients from 369 hospitals that reported at least one live birth in 2002 in six representative states.Study design
Logistic regression of death, anesthesia complication, nonanesthesia maternal complication, and obstetrical trauma for all patients and those having cesarean deliveries on anesthesia provider model, obstetrical and anesthesia, and patient variables.Data collection/extraction methods
Data was assembled from information given by hospitals to state agencies and from a 2004 survey of obstetrical organization resources.Principal findings
Anesthesia complication rates in anesthesiologist-only hospitals were 0.27 percent compared with 0.23 percent in certified registered nurse anesthetist (CRNA) only hospitals. Rates among other provider models varied from 0.24 to 0.37 percent with none statistically different from the anesthesiologist-only hospitals. A similar pattern was observed for rates of other outcomes. Multivariate analysis found no systematic differences between hospitals with anesthesiologist-only models and models using CRNAs. There was no consistent pattern of association of other hospital or patient characteristics with outcomes.Conclusion
Hospitals that use only CRNAs, or a combination of CRNAs and anesthesiologists, do not have systematically poorer maternal outcomes compared with hospitals using anesthesiologist-only models.
SUBMITTER: Needleman J
PROVIDER: S-EPMC2677049 | biostudies-literature | 2009 Apr
REPOSITORIES: biostudies-literature
Publications
Anesthesia provider model, hospital resources, and maternal outcomes.
Needleman Jack J Minnick Ann F AF
Health services research 20081104 2 Pt 1
<h4>Objective</h4>Determine the ability of anesthesia provider model and hospital resources to explain maternal outcome variation.<h4>Data source/study setting</h4>1,141,641 obstetrical patients from 369 hospitals that reported at least one live birth in 2002 in six representative states.<h4>Study design</h4>Logistic regression of death, anesthesia complication, nonanesthesia maternal complication, and obstetrical trauma for all patients and those having cesarean deliveries on anesthesia provide ...[more]
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Project description:BackgroundElectronic 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.ObjectiveThe 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.MethodsUsing 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.ResultsWe 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.ConclusionsGeneric 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.
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