Emergency department (ED) visits for falls among older adults are often sentinel events for poor health trajectories; however, challenges exist in defining fall‐related visits in the ED. Authors including HIP Investigators Dr. Brian Patterson and Dr. Maureen Smith developed and validated a simple rules‐based Natural language processing system that accurately identified falls from the text of ED physician notes.
The goal of the study was to compare performance characteristics of several fall identification strategies using EHR data from ED visits using manual chart abstraction as a gold standard.
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