ArticlePrehospital emergency care2025
Identification of Naloxone in Emergency Medical Services Data Substantially Improves by Processing Unstructured Patient Care Narratives.
Article in Prehospital emergency care, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Developing and Evaluating a Natural Language Processing Algorithm to Detect Insulin Pump and Continuous Glucose Monitor Use in Electronic Health Records of Patients with Type 1 Diabetes.Diabetes technology & therapeutics · 2026Article
- Artificial Intelligence for Opioid Safety Surveillance from Clinical Text: A Clinically Focused Review.Journal of clinical medicine · 2026Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
objectivesStructured data fields, including medication fields involving naloxone, are routinely used to identify opioid overdoses in emergency medical services (EMS) data; between January 2021 and March 2024, there were approximately 1.2 million instances of naloxone administration in the United States. To improve the accuracy of naloxone reporting, we developed methodology for identifying naloxone administration using both structured fields and unstructured patient care narratives for events documented by EMS.
methodsWe randomly sampled 30,000 records from Kentucky's state-wide EMS database during 2019. We applied regular expressions (RegEx) capable of recognizing naloxone-related text patterns in each EMS patient's case narrative. Additionally, we applied natural language processing (NLP) techniques to extract important contextual factors such as route and dosage from these narratives. We manually reviewed cases where the structured data and unstructured data disagreed and developed an aggregate indicator for naloxone administration using either structured or unstructured data for each patient case.
resultsThere were 437 (1.45%) records with structured documentation of naloxone. Our RegEx method identified 547 naloxone administrations in the narratives; after manual review, we determined RegEx yielded acceptable false positives (
conclusionsAn additional 115 (26.3%) patients receiving naloxone were identified by using unstructured case narratives compared to structured data. New surveillance methods that incorporate unstructured EMS narratives are critically needed to avoid substantial underestimation of naloxone utilization and enumeration of opioid overdoses.
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