Prehospital emergency care : official journal of the National Association of EMS Physicians and the National Association of State EMS Directors
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Clinical judgment describes the process an emergency medical service clinician uses to evaluate problems and make decisions in the out-of-hospital setting. As part of the redesign of the Advanced Life Support (ALS) certification examinations, the National Registry of Emergency Medical Technicians is developing and evaluating items that measure clinical judgment, with the intention of assessing these as a new domain in the ALS certification examinations. In this study, we provide evidence around the redesign by evaluating the reliability and validity of the advanced emergency medical technician (AEMT) and paramedic certification examinations when clinical judgment is included as a sixth domain along with the five current domains. ⋯ We demonstrate strong reliability and validity evidence to support that the integrity of the examinations is upheld with the addition of clinical judgment items, while also providing a more robust candidate evaluation. Most importantly, the pass/fail decisions that candidates receive accurately reflect their level of ALS knowledge at the entry-level.
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Emergency Medical Services (EMS) clinicians desire performance feedback (PF) and patient outcome follow-up (POF). Within our agency, both a peer-review and feedback/outcome (PF/POF) process exist. Our objective was to determine whether receiving feedback and outcome data improved future clinical care amongst EMS, based upon peer-review scores. ⋯ Providing performance feedback and patient outcome follow-up to EMS is associated with improved peer-review scores of clinical performance. Future studies should assess if those that are submitting cases for feedback/outcome are higher performers at baseline or if the process of receiving feedback/outcome improves their performance.
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Benzodiazepines are the primary antiseizure medication used by Emergency Medical Services (EMS) for seizures. Available literature in the United States and internationally shows 30% to 40% of seizures do not terminate with benzodiazepines called benzodiazepine refractory status epilepticus (BRSE). Ketamine is a potential treatment for BRSE due to its unique pharmacology. However, its application in the prehospital setting is mostly documented in case reports. Little is known about its use by EMS professionals for seizure management, whether as initial treatment or for BRSE, creating an opportunity to describe its current use and inform future research. ⋯ In this retrospective review of the ESO Data Collaborative, ketamine administration for seizure encounters without an airway procedure increased over the study period, both as a single agent and with another ASM. Most ketamine administrations were for adult patients in the south and in urban areas. The frequency of BRSE, the need for effective treatment, and the growth in ketamine use warrant prospective prehospital research to evaluate the value of ketamine in prehospital seizure management.
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The American Board of Emergency Medicine (ABEM) Emergency Medical Services Medicine (EMS) subspecialty was approved by the American Board of Medical Specialties on September 23, 2010. Subspecialty certification in EMS was contingent on two key elements-completing Accreditation Council for Graduate Medical Education (ACGME)-accredited EMS training and passing the subspecialty certification examination developed by ABEM. The first EMS certification examination was offered in October 2013. Meaningful certification requires rigorous assessment. In this instance, the EMS certification examination sought to embrace the tenets of validity, reliability, and fairness. For the purposes of this report, the sources of validity evidence were anchored on the EMS core content, the examination development process, and the association between fellowship training and passing the certification examination. ⋯ There is substantial and varied validity evidence to support the use of the EMS certifying examination in making summative decisions to award certification in EMS. Of note, there was a statistically significant association between ACGME-accredited fellowship training and passing the examination.
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This study assesses the feasibility, inter-rater reliability, and accuracy of using OpenAI's ChatGPT-4 and Google's Gemini Ultra large language models (LLMs), for Emergency Medical Services (EMS) quality assurance. The implementation of these LLMs for EMS quality assurance has the potential to significantly reduce the workload on medical directors and quality assurance staff by automating aspects of the processing and review of patient care reports. This offers the potential for more efficient and accurate identification of areas requiring improvement, thereby potentially enhancing patient care outcomes. ⋯ Large language models demonstrate potential in supporting quality assurance by effectively and objectively extracting data elements. However, their accuracy in interpreting non-standardized and time-sensitive details remains inferior to human evaluators. Our findings suggest that current LLMs may best offer supplemental support to the human review processes, but their current value remains limited. Enhancements in LLM training and integration are recommended for improved and more reliable performance in the quality assurance processes.