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The purpose of this study was to develop a method to define and rate the severity of adverse events (AEs) in emergency medical services (EMS) safety research. They used a modified Delphi technique to develop a consensus definition of an AE. The consensus definition was as follows: "An adverse event in EMS is a harmful or potentially harmful event occurring during the continuum of EMS care that is potentially preventable and thus independent of the progression of the patient's condition." Physicians reviewed 250 charts from 3 EMS agencies for AEs. The authors examined physician agreement using κ, Fleiss's κ, and corresponding 95% confidence intervals (CIs). Overall physician agreement on presence of an AE per chart was fair (κ = 0.24; 95% CI = 0.19, 0.29). These findings should serve as a basis for refining and implementing an AE evaluation instrument.

This study found a 5.6% incidence of hyponatremia in marathon runners requiring medical treatment.

Introduction We sought to create a valid framework for detecting Adverse Events (AEs) in the high-risk setting of Helicopter Emergency Medical Services (HEMS). Methods We assembled a panel of 10 expert clinicians (n=6 emergency medicine physicians and n=4 prehospital nurses and flight paramedics) affiliated with a large multi-state HEMS organization in the Northeast U.S. We used a modified Delphi technique to develop a framework for detecting AEs associated with the treatment of critically ill or injured patients. We used a widely applied measure, the Content Validity Index (CVI), to quantify the validity of the framework’s content. Results The expert panel of 10 clinicians reached consensus on a common AE definition and four-step protocol/process for AE detection in HEMS. The consensus-based framework is composed of three main components: 1) a trigger tool, 2) a method for rating proximal cause, and 3) a method for rating AE severity. The CVI findings isolate components of the framework considered content valid. Conclusions We demonstrate a standardized process for the development of a content valid framework for AE detection. The framework is a model for the development of a method for AE identification in other settings, including ground-based EMS.

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Symptomatic cerebral air embolism is a rare event during neuro-angiographic procedures. Analysis of the etiologies of this infrequent event may permit us to further reduce its incidence.