five

Script-theory virtual case: A novel tool for education and research

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DataCite Commons2020-09-04 更新2024-07-25 收录
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https://tandf.figshare.com/articles/dataset/Script_theory_virtual_case_A_novel_tool_for_education_and_research/3188809
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<b>Context/Setting:</b> The script theory of diagnostic reasoning proposes that clinicians evaluate cases in the context of an “illness script,” iteratively testing internal hypotheses against new information eventually reaching a diagnosis. We present a novel tool for teaching diagnostic reasoning to undergraduate medical students based on an adaptation of script theory. <b>Intervention:</b> We developed a virtual patient case that used clinically authentic audio and video, interactive three-dimensional (3D) body images, and a simulated electronic medical record. Next, we used interactive slide bars to record respondents’ likelihood estimates of diagnostic possibilities at various stages of the case. Responses were dynamically compared to data from expert clinicians and peers. Comparative frequency distributions were presented to the learner and final diagnostic likelihood estimates were analyzed. Detailed student feedback was collected. <b>Observations:</b> Over two academic years, 322 students participated. Student diagnostic likelihood estimates were similar year to year, but were consistently different from expert clinician estimates. Student feedback was overwhelmingly positive: students found the case was novel, innovative, clinically authentic, and a valuable learning experience. <b>Discussion:</b> We demonstrate the successful implementation of a novel approach to teaching diagnostic reasoning. Future study may delineate reasoning processes associated with differences between novice and expert responses.

**背景/设置:** 诊断推理脚本理论(script theory of diagnostic reasoning)提出,临床医生会在“疾病脚本(illness script)”的框架下评估病例,通过结合新信息反复验证内部假设,最终得出诊断。本研究基于该脚本理论的改良版本,开发了一款面向医学本科生的诊断推理教学新工具。 **干预措施:** 我们开发了一款虚拟患者病例(virtual patient case),其集成了临床真实场景音视频、交互式三维(3D)人体图像与模拟电子病历(simulated electronic medical record)。随后,我们通过交互式滑动条记录受试者在病例不同阶段的诊断可能性概率评估值,并将其与临床专家及同行的数据进行动态比对;向学习者展示比对后的频率分布情况,并对最终的诊断概率评估值开展分析,同时收集了学生的详细反馈。 **研究结果:** 在两个学年内,共有322名学生参与本研究。不同学年间学生的诊断概率评估值结果相近,但始终与临床专家的评估结果存在差异。学生反馈整体极为积极:多数学生认为该病例新颖独特、兼具创新性与临床真实性,是一次极具价值的学习体验。 **讨论:** 本研究证实了一款诊断推理教学新方法的成功落地。未来可进一步阐明新手与专家在诊断推理过程中的差异机制。
提供机构:
Taylor & Francis
创建时间:
2016-04-22
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