在团队诊断中重新定义上下文特异性,使用分布式认知理论
James G Boyle1, Matthew R Walters2, Susan Jamieson3
1Undergraduate Medical School, School of Medicine, Dentistry and Nursing, University of Glasgow, Glasgow, UK.
Diagnosis (Berlin, Germany)
|July 4, 2023
概括
医学诊断中的上下文特异性发生在情境因素导致类似患者的不同诊断时. 这项研究使用内部医学团队的分布式认知 (DCog) 重构了这一现象.
科学领域:
- 医学教育 医学教育
- 认知科学 认知科学
- 临床推理 临床推理
背景情况:
- 临床诊断中的上下文特异性不太清楚,导致诊断差异.
- 以前的研究集中在个人临床医生身上,忽视了团队动态.
- 情境因素显著影响临床推理和诊断结果.
研究的目的:
- 在以团队为基础的临床推理中重新定义上下文特异性.
- 应用分布式认知 (DCog) 的镜头来理解医疗团队的诊断差异.
- 确定背景特异性如何在团队与个人临床护理中表现得不同.
主要方法:
- 使用分布式认知 (DCog) 框架.
- 在内部医学圆形团队中分析了临床推理过程.
- 检查了随着时间的推移,意义如何在团队成员之间动态分布.
主要成果:
- 确定了四种不同的方式,背景特异性在以团队为基础的护理中发挥作用.
- 证明诊断意义在圆形化团队内动态演变.
- 展示了团队互动如何与个人推理不同地影响诊断结果.
结论:
- 诊断中的上下文特异性是一个复杂的,取决于团队的现象.
- 分布认知为理解以团队为基础的临床推理提供了一个有价值的模型.
- 这些发现对改善诊断准确性和减少跨专业医疗保健机构的差异产生影响.
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