Predicting Fall Risk from Home Health Care Assessments | Penn Nursing

Principal Investigator: Kathryn H. Bowles, PhD, RN, FAAN, FACMI

Department: Biobehavioral Health Sciences, School of Nursing

Services Provided: Data Wrangling, Data Analysis, Publication Support

Publications:

• Y Lo, SF Lynch, et. al. A Machine Learning Approach to Improve Fall Risk Prediction in Home Health Care. May 2018. Symposium on Data Science and Statistics. Reston, Virginia (Poster)
• Y Lo, SF Lynch, et. al. Using machine learning on home health care assessments to predict fall risk. Accepted March 2019. The 17th World Congress of Medical and Health Informatics. Lyon, France (Full Paper)
• Hong J, Lo Y, et al. Identifying Falls Documented in Home Health Care Clinical Notes using Natural Language Processing. ICIBM 2020. Philadelphia, PA

Description:
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 curated a home health care assessment dataset with over 100 clinical, behavioral, and cognitive features and applied a random forest algorithm to identify factors that predict and quantify fall risks. We will extend the analysis to incorporate longitudinal assessments and apply natural language processing techniques on visit notes to identify fall cases unreported in structured data. Our model achieves higher precision and balanced accuracy than the commonly used multifactorial Missouri Alliance for Home Care fall risk assessment.  This could lead to a reduction of paperwork for nursing staff and better targeting of high fall risk patients.