Dataset Information

COVID-19 and the duration of operating room procedures in Ontario: a population-based retrospective study.

ABSTRACT:

Background

Studies have estimated that a large backlog of procedures was generated by emergency measures implemented in Ontario, Canada, at the onset of the COVID-19 pandemic, when nonessential and scheduled procedures were postponed. Understanding the impact of the COVID-19 pandemic on the time needed to perform a procedure may help to determine the resources needed to tackle the substantial backlog caused by the deferral of cases. The purpose of this study was to examine the duration of operating room (OR) procedures before and after the onset of the COVID-19 pandemic to inform planning around changes in required resources.

Methods

A population-based, retrospective cohort study was conducted using Ontario Health Insurance Plan claims data and other administrative health care data from Apr. 1, 2019, to Sept. 30, 2020. Statistical analysis was conducted using multivariate regression, with procedure duration as the outcome variable.

Results

Results showed that the average duration of nonelective procedures increased by 34 minutes during the COVID-19 period and by 19 minutes after the resumption of scheduled procedures. Controlling for physician, patient and hospital characteristics, and the procedure code submitted, procedure duration increased by 12 minutes in the nonelective COVID-19 period and by 5 minutes when scheduled procedures resumed, compared with the pre-COVID-19 period.

Conclusion

Procedures may take longer in the COVID-19 period. This will affect wait times, which had already increased because of the deferral of procedures at the beginning of the pandemic, and will have an impact on Ontario's ability to provide patients with timely care.

SUBMITTER: Kantarevic J

PROVIDER: S-EPMC9564191 | biostudies-literature | 2022 Sep-Oct

REPOSITORIES: biostudies-literature

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Publications

COVID-19 and the duration of operating room procedures in Ontario: a population-based retrospective study.

Kantarevic Jasmin J Chami Nadine N Vinden Chris C Nadolski Joanna J Adamson Michael M Li Yin Y Weir Sharada S Wright James G JG McClure Andrew A Hill Samantha S

Canadian journal of surgery. Journal canadien de chirurgie 20220901 5

<h4>Background</h4>Studies have estimated that a large backlog of procedures was generated by emergency measures implemented in Ontario, Canada, at the onset of the COVID-19 pandemic, when nonessential and scheduled procedures were postponed. Understanding the impact of the COVID-19 pandemic on the time needed to perform a procedure may help to determine the resources needed to tackle the substantial backlog caused by the deferral of cases. The purpose of this study was to examine the duration o ...[more]

PMID: 36223936

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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

COVID-19 and the duration of operating room procedures in Ontario: a population-based retrospective study.

Background

Methods

Results

Conclusion

Publications

COVID-19 and the duration of operating room procedures in Ontario: a population-based retrospective study.

Similar Datasets

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