Falls among older adults are both a common reason for presentation to the emergency department, and a major source of morbidity and mortality. It is critical to identify fall patients quickly and reliably during, and immediately after, emergency department encounters in order to deliver appropriate care and referrals. Unfortunately, falls are difficult to identify without manual chart review, a time intensive process infeasible for many applications including surveillance and quality reporting. HIP Investigator, Dr. Brian Patterson described and validated a pragmatic, rules-based Natural Language Processing approach for identification of fall patients in the emergency department.
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