Development and Validation of a Machine Learning Model to Aid Discharge Processes for Inpatient Surgical Care.

ABSTRACT: Importance:Inpatient overcrowding is associated with delays in care, including the deferral of surgical care until beds are available to accommodate postoperative patients. Timely patient discharge is critical to address inpatient overcrowding and requires coordination among surgeons, nurses, case managers, and others. This is difficult to achieve without early identification and systemwide transparency of discharge candidates and their respective barriers to discharge. Objective:To validate the performance of a clinically interpretable feedforward neural network model that could improve the discharge process by predicting which patients would be discharged within 24 hours and their clinical and nonclinical barriers. Design, Setting, and Participants:This prognostic study included adult pa

SUBMITTER: Safavi KC 

PROVIDER: S-EPMC6991195 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature