Frequent Emergency Medical Services Utilization Among Older Patients: Exploration and Automatic Identification Using Natural Language Processing

An ageing population is associated with increased health care utilization and higher likelihood of older adults becoming frequent users of emergency medical services (EMS). This study aims to explore a natural language processing (NLP) approach using textual notes from EMS to facilitate the identifi...

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Vydané v:Journal of the American Medical Directors Association Ročník 26; číslo 11; s. 105814
Hlavní autori: Pouw, Maaike A, Dijkstra, Hidde, Kőntőș, Eduard-Raul, de Jonghe, Annemarieke, Dercksen, Bert, Maruster, Laura, van der Zee, Durk-Jouke, van Munster, Barbara C
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States 01.11.2025
Predmet:
Aged
Aged, 80 and over
Emergency Medical Services - statistics & numerical data
Female
Humans
Male
Natural Language Processing
Patient Acceptance of Health Care - statistics & numerical data
Retrospective Studies
clinical notes
frequent users
geriatric emergency medicine
ambulance
natural language processing
Emergency medical services
ISSN:1538-9375, 1538-9375
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Shrnutí:An ageing population is associated with increased health care utilization and higher likelihood of older adults becoming frequent users of emergency medical services (EMS). This study aims to explore a natural language processing (NLP) approach using textual notes from EMS to facilitate the identification of older patients at risk of frequent usage. Retrospective cohort study. Routinely collected patient data of patients aged ≥65 years from EMS records 2013-2019. Frequent EMS users were defined as patients with ≥3 EMS uses per year. Descriptive statistics were used for exploratory analysis. For NLP model development, data were split into training (80%) and test (20%) sets, stratified by frequent user incidence. Raw EMS text was preprocessed and transformed using term frequency-inverse document frequency. These vectors served as input to an Extreme Gradient Boosting (XGBoost) algorithm, trained with 5-fold cross validation. Model performance was evaluated using c-statistic, calibration (slope/intercept), sensitivity (recall), precision, and F1 score. Insights into word importance and example predictions were examined and provided. Among 97,736 patients (median age, 78 years; interquartile range, 72-85), 9.8% were frequent users, accounting for 28.6% of EMS use. On the training set, the supervised machine learning XGBoost algorithm achieved a c-statistic of 0.97 (95% CI, 0.97-0.97), recall of 0.93, precision of 0.47, and F1 score of 0.63. Test set performance was slightly lower, with a c-statistic of 0.92 (95% CI, 0.90-0.92), recall of 0.82, precision of 0.41, and F1 score of 0.54. Calibration metrics indicated good model fit. An NLP-based model using EMS text data could improve the identification of frequent users. Integration of such tools into prehospital care systems may support earlier identification and tailored intervention strategies for older people frequent in use of acute care.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1538-9375
1538-9375
DOI:10.1016/j.jamda.2025.105814