ArticleBMC medical informatics and decision making2019
Development and validation of a pragmatic natural language processing approach to identifying falls in older adults in the emergency department.
Article in BMC medical informatics and decision making, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 4 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 4 syntheses or guidelines pooled it, 23 citations in OpenAlex.
- Natural language processing for geriatric syndromes: a systematic review of methods, applications, and challenges.BMC medical informatics and decision making · 2026Pooled it
- The use of natural language processing for the identification of ageing syndromes including sarcopenia, frailty and falls in electronic healthcare records: a systematic review.Age and ageing · 2024Pooled it
- Using natural language processing in emergency medicine health service research: A systematic review and meta-analysis.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2024Pooled it
- The use of natural language processing in detecting and predicting falls within the healthcare setting: a systematic review.International journal for quality in health care : journal of the International Society for Quality in Health Care · 2023Pooled it
- Using AI to identify in-hospital falls: a comparative analysis with incident reports and manual chart review.BMJ open quality · 2026Article
- Identification and validation of respiratory virus immunization using natural language processing.Frontiers in digital health · 2026Article
- Performance of Natural Language Processing versus International Classification of Diseases Codes in Building Registries for Patients With Fall Injury: Retrospective Analysis.JMIR medical informatics · 2025Article
- Article
- Evaluation of falls detected by natural language processing algorithm and not coded external cause of morbidity.JAMIA open · 2025Article
- Improving postsurgical fall detection for older Americans using LLM-driven analysis of clinical narratives.medRxiv : the preprint server for health sciences · 2024Article
- Coding linguistic elements in clinical interactions: a step-by-step guide for analyzing communication form.BMC medical research methodology · 2022Article
- A hybrid model to identify fall occurrence from electronic health records.International journal of medical informatics · 2022Article
- RESEARCHComparing Strategies for Identifying Falls in Older Adult Emergency Department Visits Using EHR Data.Journal of the American Geriatrics Society · 2020Observational
- Thyroid Ultrasound Reports: Will the Thyroid Imaging, Reporting, and Data System Improve Natural Language Processing Capture of Critical Thyroid Nodule Features?The Journal of surgical research · 2020Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
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.
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