Simplified Machine Learning Models Can Accurately Identify High-Need High-Cost Patients With Inflammatory Bowel Disease.

Journal: Clinical and translational gastroenterology
Published Date: Jun 7, 2022

Abstract

INTRODUCTION: Hospitalization is the primary driver of inflammatory bowel disease (IBD)-related healthcare costs and morbidity. Traditional prediction models have poor performance at identifying patients at highest risk of unplanned healthcare utilization. Identification of patients who are high-need and high-cost (HNHC) could reduce unplanned healthcare utilization and healthcare costs.

Authors

  • Nghia H Nguyen
    Division of Gastroenterology, Department of Medicine, University of California San Diego, La Jolla, CA, USA.
  • Sagar Patel
    Division of Gastroenterology, Department of Medicine, University of California San, Diego, La Jolla, California, USA.
  • Jason Gabunilas
    Division of Gastroenterology, Department of Medicine, University of California San, Diego, La Jolla, California, USA.
  • Alexander S Qian
    Division of Gastroenterology, Department of Medicine, University of California San, Diego, La Jolla, California, USA.
  • Alan Cecil
    Division of Gastroenterology, Department of Medicine, University of California San, Diego, La Jolla, California, USA.
  • Vipul Jairath
    Department of Epidemiology and Biostatistics, Western University, London, ON, Canada.
  • William J Sandborn
    University of California San Diego, La Jolla, California.
  • Lucila Ohno-Machado
    University of California San Diego, La Jolla, CA.
  • Peter L Chen
    HyperPlanar, San Diego, CA, USA.
  • Siddharth Singh
    Division of Gastroenterology, Department of Medicine, University of California San Diego, La Jolla, CA, USA.

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