Ability of artificial intelligence to correctly predict inpatient versus observation hospital discharge status

Abstract

Objective: This study assessed the ability of a real-time artificial intelligence (AI) tool to correctly align early during hospitalization with the discharge status of inpatient versus observation. Methods: This retrospective case-control study at Baylor Scott & White Medical Center - Temple involved patients on 11 randomly chosen calendar days between August 2023 and October 2024. A real-time AI care level score (CLS) and machine learning likelihood (MeL) recommendations for inpatient versus observation discharge status were developed. Receiver operating characteristic curves were used to compare CLS, MeL, and commercial screening tool criteria with actual inpatient versus observation discharge status. Results: The receiver operating characteristic curve for CLS-based prediction of the MeL recommendation for inpatients had the highest area under the curve (AUC) of 0.9954 (95% confidence interval [CI] = 0.9954, 0.9998). The AUC for only CLS for predicting inpatient discharge was 0.8949 (95% CI = 0.8692, 0.9206). A CLS score ≥76 resulted in the highest correct classification rate of 86%. For CLS and the commercial screening tool, the AUC was the lowest at 0.8419 (95% CI = 0.8121, 0.871). Conclusions: Patients with a real-time AI CLS ≥76 had an 86% correct assignment of inpatient discharge status.

Ähnliche Arbeiten