Implementation and Continuous Monitoring of an Electronic Health Record Embedded Readmissions Clinical Decision Support Tool.

Published online

Journal Article

Unplanned hospital readmissions represent a significant health care value problem with high costs and poor quality of care. A significant percentage of readmissions could be prevented if clinical inpatient teams were better able to predict which patients were at higher risk for readmission. Many of the current clinical decision support models that predict readmissions are not configured to integrate closely with the electronic health record or alert providers in real-time prior to discharge about a patient's risk for readmission. We report on the implementation and monitoring of the Epic electronic health record-"Unplanned readmission model version 1"-over 2 years from 1/1/2018-12/31/2019. For patients discharged during this time, the predictive capability to discern high risk discharges was reflected in an AUC/C-statistic at our three hospitals of 0.716-0.760 for all patients and 0.676-0.695 for general medicine patients. The model had a positive predictive value ranging from 0.217-0.248 for all patients. We also present our methods in monitoring the model over time for trend changes, as well as common readmissions reduction strategies triggered by the score.

Full Text

Duke Authors

Cited Authors

  • Gallagher, D; Zhao, C; Brucker, A; Massengill, J; Kramer, P; Poon, EG; Goldstein, BA

Published Date

  • August 26, 2020

Published In

Volume / Issue

  • 10 / 3

PubMed ID

  • 32858890

Pubmed Central ID

  • 32858890

International Standard Serial Number (ISSN)

  • 2075-4426

Digital Object Identifier (DOI)

  • 10.3390/jpm10030103

Language

  • eng

Conference Location

  • Switzerland