Dataset Information

Identified themes of interactive visualizations overlayed onto EHR data: an example of improving birth center operating room efficiency.

ABSTRACT:

Objective

While electronic health record (EHR) systems store copious amounts of patient data, aggregating those data across patients can be challenging. Visual analytic tools that integrate with EHR systems allow clinicians to gain better insight and understanding into clinical care and management. We report on our experience building Tableau-based visualizations and integrating them into our EHR system.

Materials and methods

Visual analytic tools were created as part of 12 clinician-initiated quality improvement projects. We built the visual analytic tools in Tableau and linked it within our EPIC environment. We identified 5 visual themes that spanned the various projects. To illustrate these themes, we choose 1 exemplary project which aimed to improve obstetric operating room efficiency.

Results

Across our 12 projects, we identified 5 visual themes that are integral to project success: scheduling & optimization (in 11/12 projects); provider assessment (10/12); executive assessment (8/12); patient outcomes (7/12); and control and goal charts (2/12).

Discussion

Many visualizations share common themes. Identification of these themes has allowed our internal team to be more efficient and directed in developing visualizations for future projects.

Conclusion

Organizing visual analytics into themes can allow informatics teams to more efficiently provide visual products to clinical collaborators.

SUBMITTER: Stirling A

PROVIDER: S-EPMC7647303 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Publications

Identified themes of interactive visualizations overlayed onto EHR data: an example of improving birth center operating room efficiency.

Stirling Andrew A Tubb Tracy T Reiff Emily S ES Grotegut Chad A CA Gagnon Jennifer J Li Weiyi W Bradley Gail G Poon Eric G EG Goldstein Benjamin A BA

Journal of the American Medical Informatics Association : JAMIA 20200501 5

<h4>Objective</h4>While electronic health record (EHR) systems store copious amounts of patient data, aggregating those data across patients can be challenging. Visual analytic tools that integrate with EHR systems allow clinicians to gain better insight and understanding into clinical care and management. We report on our experience building Tableau-based visualizations and integrating them into our EHR system.<h4>Materials and methods</h4>Visual analytic tools were created as part of 12 clinic ...[more]

PMID: 32181803

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Project description:BackgroundAccurate estimation of operative case-time duration is critical for optimizing operating room use. Current estimates are inaccurate and earlier models include data not available at the time of scheduling. Our objective was to develop statistical models in a large retrospective data set to improve estimation of case-time duration relative to current standards.Study designWe developed models to predict case-time duration using linear regression and supervised machine learning. For each of these models, we generated an all-inclusive model, service-specific models, and surgeon-specific models. In the latter 2 approaches, individual models were created for each surgical service and surgeon, respectively. Our data set included 46,986 scheduled operations performed at a large academic medical center from January 2014 to December 2017, with 80% used for training and 20% for model testing/validation. Predictions derived from each model were compared with our institutional standard of using average historic procedure times and surgeon estimates. Models were evaluated based on accuracy, overage (case duration > predicted + 10%), underage (case duration < predicted - 10%), and the predictive capability of being within a 10% tolerance threshold.ResultsThe machine learning algorithm resulted in the highest predictive capability. The surgeon-specific model was superior to the service-specific model, with higher accuracy, lower percentage of overage and underage, and higher percentage of cases within the 10% threshold. The ability to predict cases within 10% improved from 32% using our institutional standard to 39% with the machine learning surgeon-specific model.ConclusionsOur study is a notable advancement toward statistical modeling of case-time duration across all surgical departments in a large tertiary medical center. Machine learning approaches can improve case duration estimations, enabling improved operating room scheduling, efficiency, and reduced costs.

Dataset Information

Identified themes of interactive visualizations overlayed onto EHR data: an example of improving birth center operating room efficiency.

Objective

Materials and methods

Results

Discussion

Conclusion

Publications

Identified themes of interactive visualizations overlayed onto EHR data: an example of improving birth center operating room efficiency.

Similar Datasets

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