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

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

Authors :
Brian W. Patterson
Gwen C. Jacobsohn
Manish N. Shah
Yiqiang Song
Apoorva Maru
Arjun K. Venkatesh
Monica Zhong
Katherine Taylor
Azita G. Hamedani
Eneida A. Mendonça
Source :
BMC Medical Informatics and Decision Making, Vol 19, Iss 1, Pp 1-8 (2019)
Publication Year :
2019
Publisher :
BMC, 2019.

Abstract

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.

Details

Language :
English
ISSN :
14726947
Volume :
19
Issue :
1
Database :
Directory of Open Access Journals
Journal :
BMC Medical Informatics and Decision Making
Publication Type :
Academic Journal
Accession number :
edsdoj.7a6c05ed40274202af911d82d4afe317
Document Type :
article
Full Text :
https://doi.org/10.1186/s12911-019-0843-7