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

• Authors: Ira L. Leeds, The Johns Hopkins Hospital; Vjollca Sadiraj, Georgia State University; James C. Cox, Georgia State University; Xiaoxue Sherry Gao, Georgia State University; Timothy M. Pawlik, The Johns Hopkins Hospital; Kurt E. Schnier, University of California; John F Sweeney, Emory University
• Journal Title: American Journal of Surgery
• Volume: Volume 213, Number 1
• Publisher: Elsevier | 2017-01-01, Pages 112-119
• Type of Work: Article | Post-print: After Peer Review
• Publisher DOI: http://doi.org/10.1016/j.amjsurg.2016.03.010
• Subjects: Health Sciences, Medicine and Surgery; Health Sciences, Public Health
• Keywords: Science & Technology; Life Sciences & Biomedicine; Surgery; Hospital readmission; Computer-assisted decision-making; Logit model; Decision support; LENGTH-OF-STAY; HOSPITAL READMISSION; COMPLICATIONS; IMPROVEMENT; QUALITY; PROGRAM

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.