Objective. Multi-patient care is important among medical trainees in an emergency department (ED). While resident efficiency is a typically measured metric, multi-patient care involves both efficiency and diagnostic / treatment accuracy. Multi-patient care ability is difficult to assess, though simulation is a potential alternative. Our objective was to generate validity evidence for a serious game in assessing multi-patient care skills among a variety of learners. Methods. This was a cross-sectional validation study using a digital serious game VitalSignsTM simulating multi-patient care within a pediatric ED. Subjects completed 5 virtual “shifts,” triaging, stabilizing, and discharging or admitting patients within a fixed time period; patients arrived at cascading intervals with pre-programmed deterioration if neglected. Predictor variables included generic multi-tasking ability, video game experience, medical knowledge, and clinical efficiency with real patients. Outcome metrics in 3 domains measured diagnostic accuracy (i.e. critical orders, diagnoses), efficiency (i.e. number of patients, time-to-order) and critical thinking (number of differential diagnoses); MANOVA determined differences between novice learners and expected expert physicians. Spearman Rank correlation determined associations between levels of expertise. Results. Ninety-five subjects’ gameplays were analyzed. Diagnostic accuracy and efficiency distinguished skill level between residency trained (residents, fellows and attendings) and pre-residency trained (medical students and undergraduate) subjects, particularly for critical orders, patients seen, and correct diagnoses (p < 0.003). There were moderate to strong correlations between the game’s diagnostic accuracy and efficiency metrics compared to level of training, including patients seen (rho = 0.47, p < 0.001); critical orders (rho = 0.80, p < 0.001); time-to-order (rho = −0.24, p = 0.025); and correct diagnoses (rho = 0.69, p < 0.001). Video game experience also correlated with patients seen (rho = 0.24, p = 0.003). Conclusion. A digital serious game depicting a busy virtual ED can distinguish between expected experts in multi-patient care at the pre- vs. post-residency level. Further study can focus on whether the game appropriately assesses skill acquisition during residency.

中文翻译：

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用于评估模拟儿科急诊科多患者护理技能的数字严肃游戏的开发和性能评估

客观的。在急诊科 (ED) 的医疗实习生中，多患者护理很重要。虽然居民效率是一个典型的衡量指标，但多病人护理涉及效率和诊断/治疗的准确性。尽管模拟是一种潜在的替代方案，但难以评估多患者护理能力。我们的目标是为评估各种学习者的多患者护理技能的严肃游戏生成有效性证据。方法。这是一项横断面验证研究，使用数字严肃游戏 VitalSignsTM 模拟儿科 ED 内的多患者护理。受试者在固定时间段内完成了 5 次虚拟“轮班”，即对患者进行分类、稳定和出院或收治；如果被忽视，患者会以级联间隔到达，并预先设定恶化。预测变量包括通用的多任务处理能力、视频游戏体验、医学知识和真实患者的临床效率。3个领域的结果指标衡量诊断准确性（即关键订单、诊断）、效率（即患者数量、订单时间）和批判性思维（鉴别诊断的数量）；MANOVA 确定了新手学习者和预期的专家医生之间的差异。斯皮尔曼等级相关性决定了专业水平之间的关联。结果。分析了 95 名受试者的游戏玩法。诊断准确性和效率区分了住院医师培训（住院医师、研究员和主治医师）和住院医师培训（医学生和本科生）科目之间的技能水平，特别是对于关键指令、患者就诊和正确诊断（p < 0.003）。与训练水平相比，游戏的诊断准确性和效率指标之间存在中等至强相关性，包括所见的患者（rho = 0.47，p < 0.001）；关键订单（rho = 0.80，p < 0.001）；订购时间（rho = -0.24，p = 0.025）；和正确的诊断（rho = 0.69，p < 0.001）。视频游戏体验也与所见患者相关（rho = 0.24，p = 0.003）。结论。描绘忙碌的虚拟急诊室的数字严肃游戏可以区分住院前和住院后水平的多患者护理方面的预期专家。进一步的研究可以集中在游戏是否适当地评估了居住期间的技能习得。关键订单（rho = 0.80，p < 0.001）；订购时间（rho = -0.24，p = 0.025）；和正确的诊断（rho = 0.69，p < 0.001）。视频游戏体验也与所见患者相关（rho = 0.24，p = 0.003）。结论。描绘忙碌的虚拟急诊室的数字严肃游戏可以区分住院前和住院后水平的多患者护理方面的预期专家。进一步的研究可以集中在游戏是否适当地评估了居住期间的技能习得。关键订单（rho = 0.80，p < 0.001）；订购时间（rho = -0.24，p = 0.025）；和正确的诊断（rho = 0.69，p < 0.001）。视频游戏体验也与所见患者相关（rho = 0.24，p = 0.003）。结论。描绘忙碌的虚拟急诊室的数字严肃游戏可以区分住院前和住院后水平的多患者护理方面的预期专家。进一步的研究可以集中在游戏是否适当地评估了居住期间的技能习得。