Development and Performance of a Clinical Decision Support Tool to Inform Resource Utilization for Elective Operations

• Published in: JAMA network open Vol. 3; no. 11; p. e2023547
• Main Authors: Goldstein, Benjamin A, Cerullo, Marcelo, Krishnamoorthy, Vijay, Blitz, Jeanna, Mureebe, Leila, Webster, Wendy, Dunston, Felicia, Stirling, Andrew, Gagnon, Jennifer, Scales, Jr, Charles D
• Format: Journal Article
• Language: English
• Published: United States American Medical Association 02-11-2020
• Subjects: Aged; Betacoronavirus; Comorbidity; Coronavirus Infections - epidemiology; Coronavirus Infections - therapy; Coronavirus Infections - virology; Coronaviruses; COVID-19; Decision Making; Decision Support Systems, Clinical; Elective Surgical Procedures; Electronic Health Records; Female; Health Care Rationing; Health Informatics; Hospitalization; Hospitals; Humans; Intensive care; Intensive Care Units; Length of Stay; Male; Middle Aged; Online Only; Original Investigation; Pandemics; Patient Discharge; Pneumonia, Viral - epidemiology; Pneumonia, Viral - therapy; Pneumonia, Viral - virology; Respiration, Artificial; Retrospective Studies; Risk Assessment; SARS-CoV-2; Severity of Illness Index; Skilled Nursing Facilities; Ventilators
• ISSN: 2574-3805; 2574-3805
• DOI: 10.1001/jamanetworkopen.2020.23547

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.
To evaluate the development and performance of a clinical decision support tool to inform resource utilization for elective procedures.
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.
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.
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.
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.