Observational studyJournal of the American Medical Informatics Association : JAMIA2021
Prediction of patient disposition: comparison of computer and human approaches and a proposed synthesis.
Observational study in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06434220 (Effect of Predictive Model on ED Physician Assessments of Patient Disposition), which is not on this map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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.
Effect of Predictive Model on ED Physician Assessments of Patient Disposition
Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A meta-analysis of the diagnostic test accuracy of artificial intelligence predicting emergency department dispositions.BMC medical informatics and decision making · 2025Pooled it
- Developing interpretable machine learning models to predict length of stay and disposition decision for adult patients in emergency departments.BMJ health & care informatics · 2025Article
- Intersectional and Marginal Debiasing in Prediction Models for Emergency Admissions.JAMA network open · 2025Article
- Suicide risk prediction for Korean adolescents based on machine learning.Scientific reports · 2025Article
- Hospitalization prediction from the emergency department using computer vision AI with short patient video clips.NPJ digital medicine · 2024Article
- Automated analysis of unstructured clinical assessments improves emergency department triage performance: A retrospective deep learning analysis.Journal of the American College of Emergency Physicians open · 2023Article
- Fair admission risk prediction with proportional multicalibration.Proceedings of machine learning research · 2023Article
- The Ability of Emergency Medical Service Staff to Predict Emergency Department Disposition: A Prospective Study.Journal of multidisciplinary healthcare · 2023Article
- Development of a low-dimensional model to predict admissions from triage at a pediatric emergency department.Journal of the American College of Emergency Physicians open · 2022Article
- Crossing the chasm from model performance to clinical impact: the need to improve implementation and evaluation of AI.NPJ digital medicine · 2022Article
- Prediction across healthcare settings: a case study in predicting emergency department disposition.NPJ digital medicine · 2021Article
- Patient safety and quality of care: a key focus for clinical informatics.Journal of the American Medical Informatics Association : JAMIA · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
objectiveTo compare the accuracy of computer versus physician predictions of hospitalization and to explore the potential synergies of hybrid physician-computer models. MATERIALS AND
methodsA single-center prospective observational study in a tertiary pediatric hospital in Boston, Massachusetts, United States. Nine emergency department (ED) attending physicians participated in the study. Physicians predicted the likelihood of admission for patients in the ED whose hospitalization disposition had not yet been decided. In parallel, a random-forest computer model was developed to predict hospitalizations from the ED, based on data available within the first hour of the ED encounter. The model was tested on the same cohort of patients evaluated by the participating physicians.
results198 pediatric patients were considered for inclusion. Six patients were excluded due to incomplete or erroneous physician forms. Of the 192 included patients, 54 (28%) were admitted and 138 (72%) were discharged. The positive predictive value for the prediction of admission was 66% for the clinicians, 73% for the computer model, and 86% for a hybrid model combining the two. To predict admission, physicians relied more heavily on the clinical appearance of the patient, while the computer model relied more heavily on technical data-driven features, such as the rate of prior admissions or distance traveled to hospital. DISCUSSION: Computer-generated predictions of patient disposition were more accurate than clinician-generated predictions. A hybrid prediction model improved accuracy over both individual predictions, highlighting the complementary and synergistic effects of both approaches.
conclusionThe integration of computer and clinician predictions can yield improved predictive performance.
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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.