Background: In charge of dispatching the ambulances, Emergency Medical Services (EMS) call center specialists often have difficulty deciding the acuity of a case given the information they can gather within a limited time. Although there are protocols to guide their decision-making, observed performance can still lack sensitivity and specificity. Machine learning models have been known to capture complex relationships that are subtle, and well-trained data models can yield accurate predictions in a split of a second.
Methods: In this study, we proposed a proof-of-concept approach to construct a machine learning model to better predict the acuity of emergency cases. We used more than 360,000 structured emergency call center records of cases received by the national emergency call center in Singapore from 2018 to 2020. Features were created using call records, and multiple machine learning models were trained.
Results: A Random Forest model achieved the best performance, reducing the over-triage rate by an absolute margin of 15% compared to the call center specialists while maintaining a similar level of under-triage rate.
Conclusions: The model has the potential to be deployed as a decision support tool for dispatchers alongside current protocols to optimize ambulance dispatch triage and the utilization of emergency ambulance resources.
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http://dx.doi.org/10.34133/hds.0008 | DOI Listing |
JMIR Med Inform
January 2025
Department of Science and Education, Shenzhen Baoan Women's and Children's Hospital, Shenzhen, China.
Background: Large language models (LLMs) have been proposed as valuable tools in medical education and practice. The Chinese National Nursing Licensing Examination (CNNLE) presents unique challenges for LLMs due to its requirement for both deep domain-specific nursing knowledge and the ability to make complex clinical decisions, which differentiates it from more general medical examinations. However, their potential application in the CNNLE remains unexplored.
View Article and Find Full Text PDFSci Adv
January 2025
Department of Marine Sciences, University of Gothenburg, Gothenburg, Sweden.
Coastal ecosystems play a major role in marine carbon budgets, but substantial uncertainties remain in the sources and fluxes of coastal carbon dioxide (CO). Here, we assess when, where, and how submarine groundwater discharge (SGD) releases CO to shallow coastal ecosystems. Time-series observations of dissolved CO and radon (Rn, a natural groundwater tracer) across 40 coastal systems from 14 countries revealed large SGD-derived CO fluxes.
View Article and Find Full Text PDFJAMA Health Forum
January 2025
Department of Emergency Medicine and Comprehensive Injury Center, Medical College of Wisconsin, Milwaukee.
Drug Saf
January 2025
Pfizer (Worldwide Medical & Safety), New York, NY, USA.
J Low Genit Tract Dis
January 2025
Division of Cancer Epidemiology & Genetics, National Cancer Institute, Rockville, MD.
Objective: The Enduring Consensus Cervical Cancer Screening and Management Guidelines Committee developed recommendations for the use of extended genotyping results in cervical cancer prevention programs.
Methods: Risks of cervical intraepithelial neoplasia grade 3 or worse were calculated using data obtained with the Onclarity HPV Assay from large cohorts. Management recommendations were based on clinical action thresholds developed for the 2019 American Society for Colposcopy and Cervical Pathology Risk-Based Management Consensus Guidelines.
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