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 readmissions (7.5%). 22 daily clinical measures were significant in both regression models. Both models demonstrated good discrimination (C statistic = 0.8 for all models). Comparison of discharge behaviors versus the predictive readmission model suggested important discordance with certain clinical measures (e.g., demographics, laboratory values) not being accounted for to optimize discharges.

Conclusions: Decision-support tools for discharge may utilize variables that are not routinely considered by healthcare providers. How providers will then respond to these atypical findings may affect implementation.

Keywords: Computer-assisted decision-making; Decision support; Hospital readmission; Logit model.

Figure 1. Comparison of the effect of observed clinical variables on predictive models of discharge and readmission

The x-axis plots the normalized regression coefficient of a time-to-event model of the likelihood of discharge for a given day’s observed clinical variables. The y-axis plots the normalized coefficient of a logit regression model of the likelihood of 30-day readmission. The sign of the readmission regression coefficient has been reversed for easier direct comparison of the variable’s effect on discharging the patient (larger regression coefficient is a higher likelihood of discharge) versus the variable’s effect on preventing readmission (larger regression coefficient is a lower likelihood of readmission). In its current projection, Quadrant I demonstrates variables that both increase the likelihood of discharge and reduce the risk of readmission; Quadrant III demonstrates variables that decrease the likelihood of discharge and increase the risk of readmission. Quadrants II and IV represent discordance between behavior and readmission with the former indicating variables that increase length of stay but reduce the risk of readmission and the latter indicating variables that decrease length of stay but increase risk of readmission. Variables found to be statistically insignificant with either model are not shown.

Figure 2. Human-machine interface of decision-support tools

This schematic illustrates that the ways in which patient data are presented to clinical decision-makers may be as important to the successful implementation of a decision-support tool as the underlying analytical methodology.

Figure 3. A regression-based algorithm predicting daily risk of readmission for a sample inpatient

The x-axis indicates elapsed days since surgery (i.e., length of stay) and the y-axis shows predicted risk of readmission if discharged on that day. The solid line represents the point estimates of readmission probabilities generated by the regression model. The dotted lines represent the 80% confidence intervals. For this specific graph, estimates were run from virtual Day 2 of admission because of the extreme unlikelihood of next day discharge for a complex surgical patient.