From the National Center for Human Factors in Healthcare.
Department of Computer Science, Georgetown University.
J Patient Saf. 2021 Dec 1;17(8):e829-e833. doi: 10.1097/PTS.0000000000000731.
Medical errors are a leading cause of death in the United States. Despite widespread adoption of patient safety reporting systems to address medical errors, making sense of the reports collected in these systems is challenging in practice. Event classification taxonomies used in many reporting systems can be complex and difficult to understand by frontline reporters, leading reporters to classify reports as "miscellaneous" as opposed to assigning a specific event-type category, which may facilitate analysis.
To assist patient safety analysts in their analysis of "miscellaneous" reports, we developed an ensemble machine learning natural language processing model to reclassify these reports. We integrated the model into a clinical workflow dashboard, evaluated user feedback, and compared differences in user thresholds for model performance.
Integrating an ensemble model to classify "miscellaneous" event reports with an interactive visualization was helpful to patient safety analysts review "miscellaneous" reports. However, patient safety analysts have different thresholds for model reclassification depending on their role and experience with "miscellaneous" event reports.
在美国，医疗差错是导致死亡的主要原因之一。尽管广泛采用了患者安全报告系统来解决医疗差错问题，但在实践中，对这些系统中收集的报告进行解读具有挑战性。许多报告系统中使用的事件分类分类法可能很复杂，并且难以被一线报告者理解，这导致报告者将报告归类为“杂项”，而不是分配特定的事件类型类别，这可能有助于分析。
为了帮助患者安全分析师分析“杂项”报告，我们开发了一个集成的机器学习自然语言处理模型来重新分类这些报告。我们将该模型集成到临床工作流程仪表板中，评估了用户反馈，并比较了模型性能的用户阈值差异。
将分类“杂项”事件报告的集成模型与交互式可视化集成在一起，对患者安全分析师审查“杂项”报告很有帮助。但是，患者安全分析师对模型重新分类的阈值因角色和对“杂项”事件报告的经验而异。
