Evaluating the Effect of a COVID-19 Predictive Model to Facilitate Discharge: A Randomized Controlled Trial

Abstract

Background: We previously developed and validated a predictive model to help clinicians identify hospitalized adults with coronavirus disease 2019 (COVID-19) who may be ready for discharge given their low risk of adverse events. Whether this algorithm can prompt more timely discharge for stable patients in practice is unknown.

Objectives: The aim of the study is to estimate the effect of displaying risk scores on length of stay (LOS).

Methods: We integrated model output into the electronic health record (EHR) at four hospitals in one health system by displaying a green/orange/red score indicating low/moderate/high-risk in a patient list column and a larger COVID-19 summary report visible for each patient. Display of the score was pseudo-randomized 1:1 into intervention and control arms using a patient identifier passed to the model execution code. Intervention effect was assessed by comparing LOS between intervention and control groups. Adverse safety outcomes of death, hospice, and re-presentation were tested separately and as a composite indicator. We tracked adoption and sustained use through daily counts of score displays.

Results: Enrolling 1,010 patients from May 15, 2020 to December 7, 2020, the trial found no detectable difference in LOS. The intervention had no impact on safety indicators of death, hospice or re-presentation after discharge. The scores were displayed consistently throughout the study period but the study lacks a causally linked process measure of provider actions based on the score. Secondary analysis revealed complex dynamics in LOS temporally, by primary symptom, and hospital location.

Conclusion: An AI-based COVID-19 risk score displayed passively to clinicians during routine care of hospitalized adults with COVID-19 was safe but had no detectable impact on LOS. Health technology challenges such as insufficient adoption, nonuniform use, and provider trust compounded with temporal factors of the COVID-19 pandemic may have contributed to the null result.

Trial registration: ClinicalTrials.gov identifier: NCT04570488.

Fig. 1

CONSORT diagram. CONSORT, Consolidated Standards of Reporting Trials extension for interventions involving AI.

Fig. 2

Adoption and sustained use of two communication channels available to clinicians. Colored dots represent the weekly total number of times the score was displayed to any user via each display channel, either a personal or shared patient list ( A ) or the patient-specific report of COVID-19 information ( B ). The weekly census of hospitalized COVID-19 patients is shown with grey dots for reference (calculated as the number of unique encounters scored by the system in that week).