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Information-gathering patterns associated with higher rates of diagnostic error

机译：与更高的诊断错误率相关的信息收集模式

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Diagnostic errors are an important source of medical errors. Problematic information-gathering is a common cause of diagnostic errors among physicians and medical students. The objectives of this study were to (1) determine if medical students' information-gathering patterns formed clusters of similar strategies, and if so (2) to calculate the percentage of incorrect diagnoses in each cluster. A total of 141 2nd year medical students completed a computer case simulation. Each student's information-gathering pattern included the sequence of history, physical examination, and ancillary testing items chosen from a predefined list. We analyzed the patterns using an artificial neural network and compared percentages of incorrect diagnoses among clusters of information-gathering patterns. We input patterns into a 35 x 35 self organizing map. The network trained for 10,000 epochs. The number of students at each neuron formed a surface that was statistically smoothed into clusters. Each student was assigned to one cluster, the cluster that contributed the largest value to the smoothed function at the student's location in the grid. Seven clusters were identified. Percentage of incorrect diagnoses differed significantly among clusters (Range 0-42%, X~2 = 13.62, P = .034). Distance of each cluster from the worst performing cluster was used to rank clusters. This rank was compared to rank determined by percentage incorrect. We found a high positive correlation (Spearman Correlation = .893, P = .007). Clusters closest to the worst performing cluster had the highest percentages of incorrect diagnoses. Patterns of information-gathering were distinct and had different rates of diagnostic error.
机译：诊断错误是医疗错误的重要来源。有问题的信息收集是医师和医学生诊断错误的常见原因。这项研究的目的是（1）确定医学生的信息收集模式是否形成了相似策略的集群，如果是，则（2）计算每个集群中错误诊断的百分比。共有141位2年级医学生完成了计算机案例模拟。每个学生的信息收集模式包括从预定列表中选择的历史记录，体格检查和辅助测试项目的顺序。我们使用人工神经网络分析了这些模式，并比较了信息收集模式集群中错误诊断的百分比。我们将模式输入到35 x 35的自组织地图中。该网络训练了10,000个纪元。每个神经元的学生人数形成了一个表面，该表面在统计上被平滑化为簇。每个学生被分配到一个聚类，该聚类为学生在网格中的位置处的平滑函数贡献了最大的价值。确定了七个集群。各组之间错误诊断的百分比差异显着（范围0-42％，X〜2 = 13.62，P = .034）。每个群集距性能最差的群集的距离用于对群集进行排名。将该排名与不正确百分比所确定的排名进行比较。我们发现一个高度正相关（Spearman相关系数= .893，P = .007）。最接近性能最差的群集的群集的错误诊断百分比最高。信息收集的模式是不同的，并且具有不同的诊断错误率。

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