Dataset Information

Development and validation of a pragmatic natural language processing approach to identifying falls in older adults in the emergency department.

ABSTRACT:

Background

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. Here we describe a pragmatic NLP approach to automating fall identification.

Methods

In 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.

Results

The 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.

Conclusions

Our 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.

SUBMITTER: Patterson BW

PROVIDER: S-EPMC6647058 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Publications

Development and validation of a pragmatic natural language processing approach to identifying falls in older adults in the emergency department.

Patterson Brian W BW Jacobsohn Gwen C GC Shah Manish N MN Song Yiqiang Y Maru Apoorva A Venkatesh Arjun K AK Zhong Monica M Taylor Katherine K Hamedani Azita G AG Mendonça Eneida A EA

BMC medical informatics and decision making 20190722 1

<h4>Background</h4>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 ...[more]

PMID: 31331322

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Dataset Information

Development and validation of a pragmatic natural language processing approach to identifying falls in older adults in the emergency department.

Background

Methods

Results

Conclusions

Publications

Development and validation of a pragmatic natural language processing approach to identifying falls in older adults in the emergency department.

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