Abstract
BackgroundFalls 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. Here we describe a pragmatic NLP approach to automating fall identification.MethodsIn this single center retrospective review, 500 emergency department provider notes from older adult patients (age 65 and older) were randomly selected for analysis. A simple, rules-based NLP algorithm for fall identification was developed and evaluated on a development set of 1084 notes, then compared with identification by consensus of trained abstractors blinded to NLP results.ResultsThe NLP pipeline demonstrated a recall (sensitivity) of 95.8%, specificity of 97.4%, precision of 92.0%, and F1 score of 0.939 for identifying fall events within emergency physician visit notes, as compared to gold standard manual abstraction by human coders.ConclusionsOur pragmatic NLP algorithm was able to identify falls in ED notes with excellent precision and recall, comparable to that of more labor-intensive manual abstraction. This finding offers promise not just for improving research methods, but as a potential for identifying patients for targeted interventions, quality measure development and epidemiologic surveillance.
Highlights
Falls among older adults are both a common reason for presentation to the emergency department, and a major source of morbidity and mortality
The first assessment occurred at the completion of the reviewers’ manual abstraction training on a subset of 50 provider notes used during the algorithm development phase—at which point reviewers demonstrated 94.0% agreement
Incidence of falls Of the notes in the test set, 24.0% were consensus coded by reviewers as a positive instance of a fall (120 of 500)
Summary
Falls among older adults are both a common reason for presentation to the emergency department, and a major source of morbidity and mortality. Previous work studying falls commonly utilizes ICD-9 and 10 diagnostic codes to identify falls in both single center and large datasets given the ready availability of diagnosis data [9,10,11,12,13]. This is a standard procedure for identifying conditions within outcomes and health services research, it may miss many patients, in the ED, where fall visits may result in other diagnosis codes reflecting the injury sustained (e.g., fractures, contusions, head trauma) without mention of the mechanism of injury. Falls offer a characteristic example of a difficult to classify “syndromic” presentation, and given their immense public health burden are an ideal use case for developing novel methods of identification
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