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Development and Performance of a Clinical Decision Support Tool to Inform Resource Utilization for Elective
Benjamin A Goldstein1,2,3,4, Marcelo Cerullo5, Vijay Krishnamoorthy6,7
1Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina.
Importance:
Hospitals ceased most elective procedures during the height of coronavirus disease 2019 (COVID-19) infections. As hospitals begin to recommence elective procedures, it is necessary to have a means to assess how resource intensive a given case may be.
Objective:
To evaluate the development and performance of a clinical decision support tool to inform resource utilization for elective procedures.
Design, Setting, And Participants:
In this prognostic study, predictive modeling was used on retrospective electronic health records data from a large academic health system comprising 1 tertiary care hospital and 2 community hospitals of patients undergoing scheduled elective procedures from January 1, 2017, to March 1, 2020. Electronic health records data on case type, patient demographic characteristics, service utilization history, comorbidities, and medications were and abstracted and analyzed. Data were analyzed from April to June 2020.
Main Outcomes And Measures:
Predicitons of hospital length of stay, intensive care unit length of stay, need for mechanical ventilation, and need to be discharged to a skilled nursing facility. These predictions were generated using the random forests algorithm. Predicted probabilities were turned into risk classifications designed to give assessments of resource utilization risk.
Results:
Data from the electronic health records of 42 199 patients from 3 hospitals were abstracted for analysis. The median length of stay was 2.3 days (range, 1.3-4.2 days), 6416 patients (15.2%) were admitted to the intensive care unit, 1624 (3.8%) received mechanical ventilation, and 2843 (6.7%) were discharged to a skilled nursing facility. Predictive performance was strong with an area under the receiver operator characteristic ranging from 0.76 to 0.93. Sensitivity of the high-risk and medium-risk groupings was set at 95%. The negative predictive value of the low-risk grouping was 99%. We integrated the models into a daily refreshing Tableau dashboard to guide decision-making.
Conclusions And Relevance:
The clinical decision support tool is currently being used by surgical leadership to inform case scheduling. This work shows the importance of a learning health care environment in surgical care, using quantitative modeling to guide decision-making.
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