Using Machine Learning on Home Health Care Assessments to Predict Fall Risk.

Journal: Studies in health technology and informatics
PMID: 31438011

Abstract

Falls are the leading cause of injuries among older adults, particularly in the more vulnerable home health care (HHC) population. Existing standardized fall risk assessments often require supplemental data collection and tend to have low specificity. We applied a random forest algorithm on readily available HHC data from the mandated Outcomes and Assessment Information Set (OASIS) with over 100 items from 59,006 HHC patients to identify factors that predict and quantify fall risks. Our ultimate goal is to build clinical decision support for fall prevention. Our model achieves higher precision and balanced accuracy than the commonly used multifactorial Missouri Alliance for Home Care fall risk assessment. This is the first known attempt to determine fall risk factors from the extensive OASIS data from a large sample. Our quantitative prediction of fall risks can aid clinical discussions of risk factors and prevention strategies for lowering fall incidence.

Authors

  • Yancy Lo
    Clinical Research Informatics Core, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA.
  • Selah F Lynch
    Clinical Research Informatics Core, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA.
  • Ryan J Urbanowicz
    Clinical Research Informatics Core, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA.
  • Randal S Olson
  • Ashley Z Ritter
    NewCourtland Center for Transitions and Health, School of Nursing, University of Pennsylvania, Philadelphia, PA, USA.
  • Christina R Whitehouse
    NewCourtland Center for Transitions and Health, School of Nursing, University of Pennsylvania, Philadelphia, PA, USA.
  • Melissa O'Connor
    Fitzpatrick College of Nursing, Villanova University, Villanova, PA, USA.
  • Susan K Keim
    NewCourtland Center for Transitions and Health, School of Nursing, University of Pennsylvania, Philadelphia, PA, USA.
  • Margaret McDonald
    Center for Home Care Policy & Research, Visiting Nurse Service New York, New York, NY, USA.
  • Jason H Moore
    University of Pennsylvania, Philadelphia, PA, USA.
  • Kathryn H Bowles
    Visiting Nurse Service of New York, NY, USA; School of Nursing, University of Pennsylvania, PA, USA.

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