Prediction of patient disposition: comparison of computer and human approaches and a proposed synthesis. (19th May 2021)

• Record Type: Journal Article
• Authors: Barak-Corren, Yuval; Agarwal, Isha; Michelson, Kenneth A; Lyons, Todd W; Neuman, Mark I; Lipsett, Susan C; Kimia, Amir A; Eisenberg, Matthew A; Capraro, Andrew J; Levy, Jason A; Hudgins, Joel D; Reis, Ben Y; Fine, Andrew M
• Is Part Of: Journal of the American Medical Informatics Association. Volume 28:Number 8(2021)
• Page Start: 1736
• Page End: 1745
• Publication Date: 2021-05-19
• Subjects: emergency medicine -- machine learning -- prediction, decision support, human-computer interaction; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocab076
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: To compare the accuracy of computer versus physician predictions of hospitalization and to explore the potential synergies of hybrid physician–computer models. Materials and Methods: A 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. Results: 198 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. Conclusion: The integration of computer and clinician predictions can yield improved predictive performance.