Discharge decision-making after complex surgery: Surgeon behaviors compared to predictive modeling to reduce surgical readmissions.

ABSTRACT:

Background

Little is known about how information available at discharge affects decision-making and its effect on readmission. We sought to define the association between information used for discharge and patients' subsequent risk of readmission.

Methods

2009-2014 patients from a tertiary academic medical center's surgical services were analyzed using a time-to-event model to identify criteria that statistically explained the timing of discharges. The data were subsequently used to develop a time-varying prediction model of unplanned hospital readmissions. These models were validated and statistically compared.

Results

The predictive discharge and readmission regression models were generated from a database of 20,970 patients totaling 115,976 patient-days with 1,565

SUBMITTER: Leeds IL 

PROVIDER: S-EPMC5362294 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature