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 multistate HEMS organization in the Northeast US. 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.
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 multistate HEMS organization in the Northeast US. 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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