Department of Community-Oriented Medical Education, Chiba University Graduate School of Medicine, 1-8-1 Inohana, Chuo-ku, Chiba City, Chiba Prefecture, Japan.
Department of General Medicine, Chiba University Hospital, Chiba, Japan.
BMC Med Educ. 2024 Nov 30;24(1):1402. doi: 10.1186/s12909-024-06395-x.
The general medicine in-training examination (GM-ITE) assesses physicians' clinical knowledge. This study expanded on findings from a previous pilot study to assess the relationship between general medicine in-training examination (GM-ITE) scores and the diagnostic skills of resident physicians in Japan by employing an innovative clinical simulation video (CSV-IE).
This multicenter cross-sectional study included 4,677 resident physicians who took the GMITE between January 17 and 30, 2023. Participants watched the CSV-IE, depicting an emergency room scenario, and provided a diagnosis. The CSV-IE depicts an emergency case and provides a diagnosis. Discrimination indices were used to assess the CSV-IE's effectiveness across clinical competence domains, and multilevel logistic regression was used to analyze physician- and hospital-level factors associated with correct diagnoses.
Correct diagnoses were provided by 470 participants (10.0%). The CSV-IE demonstrated high discriminatory power across all assessed domains, including basic clinical knowledge (DI = 0.44), symptomatology and clinical reasoning (DI = 0.31), physical examination and clinical procedure (DI = 0.35), and knowledge about the disease (DI = 0.25), supporting its utility as an effective assessment tool. In the multivariable analysis, factors associated with a higher likelihood of providing a correct CSV-IE diagnosis included a higher annual number of emergency outpatients (adjusted odds ratio: 1.025; 95% confidence interval [CI]: 1.003-1.047; P = .0230) and being in a higher postgraduate year (adjusted odds ratio: 1.387; 95% CI: 1.104-1.742; P = .005). Conversely, resident physicians at university hospitals were less likely to provide a correct CSV-IE response (adjusted odds ratio: 0.624; 95% CI: 0.435-0.896; P = .0107).
CSV-IE modules may provide an integrative and realistic evaluation of clinical competence, addressing limitations of traditional MCQ-based assessments by offering contextualized, real-world scenarios that require dynamic decision-making and diagnostic reasoning.
通用医学培训考试（GM-ITE）评估医生的临床知识。本研究在前瞻性试点研究的基础上进一步探讨了通用医学培训考试（GM-ITE）成绩与日本住院医师诊断技能之间的关系，采用了创新的临床模拟视频（CSV-IE）。
本多中心横断面研究纳入了 2023 年 1 月 17 日至 30 日参加 GMITE 的 4677 名住院医师。参与者观看了 CSV-IE，描绘了一个急诊室场景，并提供了诊断。CSV-IE 描绘了一个急诊病例，并提供了诊断。采用鉴别指数评估 CSV-IE 在各临床能力领域的有效性，并采用多水平逻辑回归分析与正确诊断相关的医师和医院水平因素。
470 名参与者（10.0%）提供了正确诊断。CSV-IE 在所有评估领域均具有较高的鉴别力，包括基础临床知识（DI=0.44）、症状学和临床推理（DI=0.31）、体格检查和临床操作（DI=0.35）以及疾病知识（DI=0.25），支持其作为一种有效的评估工具。多变量分析显示，与提供正确 CSV-IE 诊断的可能性较高相关的因素包括每年急诊门诊量较高（调整后的优势比：1.025；95%置信区间[CI]：1.003-1.047；P=0.0230）和处于较高的住院医师年资（调整后的优势比：1.387；95%CI：1.104-1.742；P=0.005）。相反，大学附属医院的住院医师提供正确 CSV-IE 反应的可能性较低（调整后的优势比：0.624；95%CI：0.435-0.896；P=0.0107）。
CSV-IE 模块可能提供综合和现实的临床能力评估，通过提供需要动态决策和诊断推理的情境化、真实世界场景，解决了传统基于 MCQ 的评估的局限性。
