Implementation of Artificial Intelligence-Based Clinical Decision Support to Reduce Hospital Readmissions at a Regional Hospital.

Journal: Applied clinical informatics
Published Date: Sep 2, 2020

Abstract

BACKGROUND: Hospital readmissions are a key quality metric, which has been tied to reimbursement. One strategy to reduce readmissions is to direct resources to patients at the highest risk of readmission. This strategy necessitates a robust predictive model coupled with effective, patient-centered interventions.

Authors

  • Santiago Romero-Brufau
    Mayo Clinic Kern Center for the Science of Health Care Delivery, Mayo Clinic, Minnesota, United States; Department of Biostatistics, Harvard T. H. Chan School of Public Health, Harvard University, Massachussets, United States. Electronic address: RomeroBrufau.Santiago@mayo.edu.
  • Kirk D Wyatt
    Division of Pediatric Hematology/Oncology, Department of Pediatric and Adolescent Medicine, Mayo Clinic, Minnesota, United States.
  • Patricia Boyum
    Mayo Clinic Kern Center for the Science of Health Care Delivery, Mayo Clinic, Minnesota, United States.
  • Mindy Mickelson
    Mayo Clinic Kern Center for the Science of Health Care Delivery, Mayo Clinic, Minnesota, United States.
  • Matthew Moore
    Mayo Clinic Kern Center for the Science of Health Care Delivery, Mayo Clinic, Minnesota, United States.
  • Cheristi Cognetta-Rieke
    Department of Nursing, Mayo Clinic Health System, La Crosse, United States.

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