Key Points Question How can clinical departments implement a clinical decision support tool to predict expected resource use to prioritize elective inpatient surgical procedures? Findings In this prognostic study, predictive models for length of stay, intensive care unit length of stay, mechanical ventilator requirement, and discharge disposition to a skilled nursing facility were developed using historical case data abstracted from the electronic health records of 42 199 patients. These models were integrated into an interactive online dashboard with end-user input and iteratively tested. Meaning Predictive modeling, in conjunction with other contextualizing factors, can be used to inform how to recommence elective inpatient procedures after the coronavirus disease 2019 (COVID-19) pandemic. 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. In this prognostic study, the development and performance of a clinical decision support tool to inform resource utilization for elective procedures requiring inpatient care were evaluated.
【초록키워드】 COVID-19, coronavirus disease, pandemic, intensive care, mechanical ventilation, hospital, Comorbidities, risk, surgical, discharge, Electronic health record, Probability, Health, infections, Predictive model, Algorithm, Patient, mechanical ventilator, Factors, Community, health system, prognostic, assessment, medication, resource, Quantitative, Intensive, Care, patients, predict, retrospective, Analysis, Negative predictive value, demographic characteristics, Predictive, Support, measure, clinical decision, participant, finding, random, objective, setting, Receiver operator characteristic, Result, tested, analyzed, was used, evaluate, evaluated, median, can be used, expected, discharged, Importance, Point, Relevance, 【제목키워드】 clinical, utilization, development, performance, operation, decision, Elective,