Trial reportApplied clinical informatics2022
Evaluating the Effect of a COVID-19 Predictive Model to Facilitate Discharge: A Randomized Controlled Trial.
Trial report in Applied clinical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04570488 (Predicting Favorable Outcomes in Hospitalized Covid-19 Patients), which is not on this map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Predicting Favorable Outcomes in Hospitalized Covid-19 Patients
Who cites it
3 citing papers in PubMed, 6 citations in OpenAlex.
- The Influence of Artificial Intelligence Scribes on Clinician Experience and Efficiency among Pediatric Subspecialists: A Rapid, Randomized Quality Improvement Trial.Applied clinical informatics · 2025Trial
- Effectiveness of a Vendor Predictive Model for the Risk of Pediatric Asthma Exacerbation: A Difference-in-Differences Analysis.Applied clinical informatics · 2023Article
- DEPLOYR: a technical framework for deploying custom real-time machine learning models into the electronic medical record.Journal of the American Medical Informatics Association : JAMIA · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundWe 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.
objectivesThe aim of the study is to estimate the effect of displaying risk scores on length of stay (LOS).
methodsWe 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.
resultsEnrolling 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.
conclusionAn 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 registrationClinicalTrials.gov identifier: NCT04570488.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.