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测量直升机紧急医疗服务中的不良事件:建立内容效度。

Measuring adverse events in helicopter emergency medical services: establishing content validity.

机构信息

From the Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.

出版信息

Prehosp Emerg Care. 2014 Jan-Mar;18(1):35-45. doi: 10.3109/10903127.2013.818179. Epub 2013 Sep 4.

Abstract

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.

摘要

简介

我们旨在为直升机紧急医疗服务(HEMS)的高危环境中检测不良事件(AE)创建一个有效的框架。

方法

我们召集了一个由 10 名临床专家组成的小组(6 名急诊医师和 4 名院前护士和飞行护理人员),隶属于美国东北部的一家大型多州 HEMS 组织。我们使用改良 Delphi 技术来开发一个用于检测与治疗危重症或受伤患者相关的 AE 的框架。我们使用了一种广泛应用的衡量标准,内容有效性指数(CVI),来量化框架内容的有效性。

结果

10 名临床专家小组就 AE 的共同定义和 HEMS 中 AE 检测的四步方案/流程达成共识。基于共识的框架由三个主要部分组成:(1)触发工具,(2)近端原因评估方法,和(3)AE 严重程度评估方法。CVI 的结果确定了框架中被认为内容有效的部分。

结论

我们展示了一种用于开发 AE 检测内容有效的框架的标准化流程。该框架是为其他环境(包括地面 EMS)中 AE 识别方法的开发提供了一个模型。

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