Please join us for a Joint GWU/CTSI-CN/Washington DC VA Informatics Seminar taking place Wednesday, December 11th, 1:00pm - 2:00pm Eastern Standard Time. Dr. Danielle Scharp of The Mount Sinai Hospital will present:
Leveraging Natural Language Processing to Identify Symptom Clusters and Risk for Hospitalizations among Older Adults with Urinary Incontinence in Home Healthcare
Background: Persistently elevated hospitalization rates in the home healthcare setting indicate the need to prioritize patients with undertreated conditions that can lead to negative outcomes. Urinary incontinence affects approximately 40% of older adults in home healthcare, yet often remains unaddressed. This leaves older adults with urinary incontinence at risk for potentially serious complications that can lead to emergency department visits, hospitalizations, and mortality. Multiple comorbidities, co-occurring symptoms, and disparities in care fuel the complexity of older adults in the home healthcare setting. The overall purpose of this project was to leverage natural language processing to understand symptom clusters and factors associated with acute care utilization among older adults with urinary incontinence in home healthcare to improve comprehensive assessment, treatment, and outcomes.
Methods: We conducted: 1) a secondary analysis of cross-sectional electronic health record data using natural language processing to extract symptoms from free-text clinical notes and analyze differences by race or ethnicity using Chi-square tests and logistic regression models, 2) a secondary analysis of cross-sectional electronic health record data using hierarchical clustering to analyze the natural language processing-extracted symptom variables, and 3) a retrospective secondary analysis of electronic health record data to identify symptom clusters and factors associated with acute care utilization using Chi-square tests and backward stepwise logistic regression.
Results: In the natural language processing study, we identified eight symptoms of older adults with urinary incontinence (i.e., anxiety, constipation, dizziness, syncope, tachycardia, urinary frequency/urgency, urinary hesitancy/retention, and vision impairment/blurred vision) that were extracted from free-text clinical notes from approximately 29% of home healthcare episodes. Compared to White patients, home healthcare episodes for Asian/Pacific Islander, Hispanic, and Black patients were less likely to have any symptoms documented in clinical notes. In the clustering analysis, we identified five distinct symptom clusters: Cluster 1 (anxiety), Cluster 2 (all symptoms), Cluster 3 (dizziness and anxiety), Cluster 4 (constipation, anxiety, and dizziness), and Cluster 5 (no symptoms). Finally, in the retrospective analysis, we found that Clusters 1-4 had higher odds of emergency department visits or hospitalizations, compared to Cluster 5.
Bio: Dr. Scharp is a postdoctoral fellow at the Icahn School of Medicine at Mount Sinai. She received a PhD in Nursing from Columbia University in 2024.