Frequent Emergency Medical Services Utilization Among Older Patients: Exploration and Automatic Identification Using Natural Language Processing.
Journal of the American Medical Directors Association · 2025-08-26 · Observational study
Abstract
OBJECTIVES: 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. DESIGN: Retrospective cohort study. SETTING AND PARTICIPANTS: Routinely collected patient data of patients aged ≥65 years from EMS records 2013-2019. METHODS: 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. RESULTS: 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. CONCLUSION AND IMPLICATIONS: 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.
Abstract © the authors or publisher, via PubMed. Reuse and removal
Tags
Decision support tools · Frequent callers · Older adults
Related articles
- Older Adult Frequent 9-1-1 Callers for Emergency Medical Services in a Large Metropolitan City: Individual- and System-Level Considerations. (J Emerg Med, 2023)
- Use of prehospital emergency medical services according to income of residential area. (Emerg Med J, 2020)
- Reducing 9-1-1 Emergency Medical Service Calls By Implementing A Community Paramedicine Program For Vulnerable Older Adults In Public Housing In Canada: A Multi-Site Cluster Randomized Controlled Trial. (Prehosp Emerg Care, 2019)
- Frequent users of the ambulance service in a Swedish region: a retrospective cohort study. (Scand J Trauma Resusc Emerg Med, 2026)
- Modeling IN out-of-hospital emergency medical services-a scoping review of approaches and applications. (Front Public Health, 2026)
- Machine Learning in Medical Emergencies: a Systematic Review and Analysis. (J Med Syst, 2021)