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Emergency Medical Services

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Evaluation of correctness and reliability of GPT, Bard, and Bing chatbots' responses in basic life support scenarios.

Scientific reports
Timely recognition and initiation of basic life support (BLS) before emergency medical services arrive significantly improve survival rates and neurological outcomes. In an era where health information-seeking behaviors have shifted toward online sou...

Utilizing machine learning and geographic analysis to improve Post-crash traffic injury management and emergency response systems.

International journal of injury control and safety promotion
Traffic injuries are a major public health concern globally. This study uses machine learning (ML) and geographic analysis to analyse road traffic fatalities and improve traffic safety in Nakhon Ratchasima Province, Thailand. Data on road traffic fat...

Prehospital triage of trauma patients: predicting major surgery using artificial intelligence as decision support.

The British journal of surgery
BACKGROUND: Matching the necessary resources and facilities to attend to the needs of trauma patients is traditionally performed by clinicians using criteria-directed triage protocols. In the present study, it was hypothesized that an artificial inte...

Evaluating the predictive performance of different data sources to forecast overdose deaths at the neighborhood level with machine learning in Rhode Island.

Preventive medicine
OBJECTIVES: To evaluate the predictive performance of different data sources to forecast fatal overdose in Rhode Island neighborhoods, with the goal of providing a template for other jurisdictions interested in predictive analytics to direct overdose...

Understanding EMS response times: a machine learning-based analysis.

BMC medical informatics and decision making
BACKGROUND: Emergency Medical Services (EMS) response times are critical for optimizing patient outcomes, particularly in time-sensitive emergencies. This study explores the multifaceted determinants of EMS response times, leveraging machine learning...

Artificial intelligence for weight estimation in paediatric emergency care.

BMJ paediatrics open
OBJECTIVE: To develop and validate a paediatric weight estimation model adapted to the characteristics of the Spanish population as an alternative to currently extended methods.

Ambulance route optimization in a mobile ambulance dispatch system using deep neural network (DNN).

Scientific reports
The ambulance dispatch system plays a crucial role in emergency medical care by ensuring efficient communication, reducing response times, and ultimately saving lives. Delays in ambulance arrival can have serious consequences for patient health and s...

Bridging Data Gaps in Emergency Care: The NIGHTINGALE Project and the Future of AI in Mass Casualty Management.

Journal of medical Internet research
In the context of mass casualty incident (MCI) management, artificial intelligence (AI) represents a promising future, offering potential improvements in processes such as triage, decision support, and resource optimization. However, the effectivenes...

Improving prediction accuracy of hospital arrival vital signs using a multi-output machine learning model: a retrospective study of JSAS-registry data.

BMC emergency medicine
BACKGROUND: Critically ill patients can deteriorate rapidly; therefore, prompt prehospital interventions and seamless transition to in-hospital care upon arrival are crucial for improving survival. In Japan, helicopter emergency medical services (HEM...

Assessment and Integration of Large Language Models for Automated Electronic Health Record Documentation in Emergency Medical Services.

Journal of medical systems
Automating Electronic Health Records (EHR) documentation can significantly reduce the burden on care providers, particularly in emergency care settings where rapid and accurate record-keeping is crucial. A critical aspect of this automation involves ...