Leveraging Data Science and Informatics in an Automated Detection System of Surgical Errors

技术的进步继续改善手术结果。然而，这些 技术也带来了新的挑战，如通信复杂性、设备 压力大，对OR团队成员有较高的认知要求。在……里面 换句话说，尽管技术有所改进，手术仍将是有风险的。的确有 有证据表明，可避免的并发症的数量可能被低估了，约39% 的住院不良事件与手术有关，多达4,000人从未手术 事件(不应该发生的事件)每年都会在美国发生。 本研究的最终目标是开发一种高性能的自动检测系统(ADS) 危险的外科手术状态。ADS将在手术安全事件发生之前通过 实时监控并提前通知适当的手术室(OR)团队成员- 如果有迫在眉睫的风险，那是时候了。从而允许团队重新考虑下一步和 解决潜在的问题，从而降低手术结果的阴性率。 本项目论证了ADS关键部件的可行性和优点。 具体地说，手术安全文献提供了令人信服的证据，表明手术工作流程 中断(FD)序列是错误原因的信息性指标，因此很可能 未来的ADS将通过跟踪流量中断来模拟和监控手术状态。我们目前的情况 目标是(1)完成研究和探索性分析驱动时间数据的实施 可视化(Read-TV)研究工具；可视化FD模式和其他的开源软件 纵向数据。(2)开发一个随机模型来预测高风险、破坏性的FD是否 序列将基于较早时间点的FD率发生。(3)将FD图案和 通过开发文本分类器来识别是否存在 根据相关的电子病历记录，发生手术安全事故或险些失手。量词 将是一个深度学习模型，用数以万计的外科电子病历笔记进行训练。 第三个目标中的语篇分析将提供对FD类型和序列的洞察，这些类型和序列 容易出错，从而揭示广告应该警告OR团队避免的FD模式。 这一文本分析的其他好处包括可能确认 事件报告不足。 在完成三个目标后，我们将有一个广告的计算基础：我们的 研究工具(目标1：阅读电视可视化软件)和分析(目标3：链路流量中断 通过EHR笔记分析的安全事件)将推进对流量中断(FD)的解释 序列和我们的随机模型(目标2：从FD序列预测未来的手术状态) 将前瞻性地预测容易出错的状态。这个基础可以在未来的项目中扩展 通过研究自动转录的流程中断，以及正确的警报模式 如果手术容易进入易出错状态，则分娩。

Technological advancements continue to improve surgical outcomes. However, these technologies also introduce new challenges such as communication complexities, equipment troubleshooting under intense pressure, and higher cognitive demand on OR team members. In other words, surgery will continue to be risky despite technological improvements. There is evidence the number of avoidable complications may be underreported, that approximately 39% of in-hospital adverse events are surgical related, and that as many as 4,000 surgical never events (events which should not have occurred) happen in the US each year. The eventual goal of this research is to develop an automated detection system (ADS) of high- risk surgical states. The ADS will prevent surgical safety incidents before they occur through real-time monitoring and notification of appropriate operating room (OR) team members ahead- of-time if there is a looming risk. Thereby allowing the team to reconsider next steps and address the underlying issues, and hence reduce the rates of negative surgical outcomes. This project demonstrates the feasibility and merit of essential components for an ADS. Specifically, the surgical safety literature provides compelling evidence that surgical work-flow disruption (FD) sequences are informative indicators of error causation, therefore it is likely that a future ADS will model and monitor surgical state through tracking flow disruptions. Our current aims are to (1) finish implementation of the Research & Exploratory Analysis Driven Time-data Visualization (READ-TV) research tool; open-source software to visualize FD patterns and other longitudinal data. (2) Develop a stochastic model to predict whether high-risk, disruptive FD sequences will occur based on FD rates at earlier time points. (3) Link FD patterns and sequences with surgical outcomes by developing a text classifier to identify whether or not a surgical safety incident or near-miss occurred based on the associated EHR note. The classifier will be a deep learning model trained with tens of thousands of surgical EHR notes. The text analysis in the third aim will provide insight to FD types and sequences that are more error prone, thereby revealing the FD patterns that an ADS should warn an OR team to avoid. Additional benefits of this text analysis include a possible confirmation of the existence of incident underreporting. Upon completion of the 3 aims, we will have a computational foundation for an ADS: our research tool (aim 1: READ-TV visualization software) and analyses (aim 3: link flow disruptions to safety incidents through EHR note analysis) will advance interpretation of flow disruption (FD) sequences, and our stochastic models (aim 2: predict future surgical state from FD sequences) will prospectively predict error-prone states. This foundation can be extended in future projects through research in automatic transcription of flow disruptions, and the proper mode of alert delivery if the surgery is prone to enter an error-prone state.