DEVELOPING AND VALIDATING EHR-INTEGRATED READMISSION RISK PREDICTION MODELS FOR HOSPITALIZED PATIENTS WITH DIABETES (eDERRI)

Study PI(S)/Affiliation:

  • Dan Rubin, Temple University/Temple University Health System
  • Anuradha Paranjape - Temple University/Temple University Health System

Protocol PI(S)/Affiliation:

  • Mary Kortikowski, University of Pittsburgh
  • Wenke Hwang, Penn State University/Milton S. Hershey Medical Center
  • H. Lester Kirchner - Geisinger

Purpose/Goal(s): Hospital readmission is an undesirable, costly outcome for both patients and hospitals that is often preventable. Patients with diabetes are at higher risk of readmission within 30 days of hospital discharge than patients without diabetes.

This project aims to develop new versions of the DERRI model enhanced by EHR (Electronic Health Records) data (eDERRI) that predict unplanned 30-day all-cause hospital readmission risk among patients with diabetes.
Using data from all PaTH sites the aims are:

  • To build an automated eDERRI tool integrated with an EHR in EPIC at Temple
  • To prospectively validate the eDERRI tool at two sites with different EHR platforms
    • at Temple in EPIC and at Penn State in Cerner

and will build an automated, EHR-integrated eDERRI tool that automatically captures EHR data to predict readmission risk

Study Design: Data on the study population will be drawn from PaTH. Through the PaTH infrastructure, data will be collected from more than 40 academic, community, and specialty hospitals. Subjects will be identified using data in the PaTH Common Data Model and will not be contacted directly.

PaTH Partners:

  • Temple University/Temple University Health System
  • University of Pittsburgh/UPMC
  • Penn State University/Milton S. Hershey Medical Center
  • Geisinger

Sponsor: National Institute of Health

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