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

在手术错误自动检测系统中利用数据科学和信息学

• 批准号: 10402771
• 负责人: John Delgaizo
• 金额: $ 1.23万
• 依托单位: MEDICAL UNIVERSITY OF SOUTH CAROLINA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-05 至 2022-08-04
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10402771
• 关键词: Address; Adverse event; Artificial Intelligence; Characteristics; Clinical; Code; Cognitive; Communication; Communities; Companions; Computer Analysis; Computer software; Custom; Data; Data Science; Detection; Electronic Health Record; Engineering; Ensure; Equipment; Etiology; Event; Foundations; Future; Genetic Transcription; Goals; Grant; Hospitals; Informatics; Information Systems; Lead; Link; Literature; Manuals; Modeling; Monitor; Natural Language Processing; Nature; Notification; Operating Rooms; Operative Surgical Procedures; Outcome; Patient-Focused Outcomes; Patients; Pattern; Prevalence; Procedures; Publishing; Reporting; Research; Research Personnel; Risk; Safety; Series; Source Code; Surgical Error; System; Technology; Testing; Text; Time; Training; Update; Vision; Visualization; Visualization software; Work; adverse outcome; base; data visualization; data warehouse; deep learning model; demographics; detection platform; high risk; improved; insight; large datasets; machine learning model; machine learning prediction; member; novel; open source; operation; predictive modeling; pressure; prevent; prospective; real time monitoring; robot assistance; surgery outcome; surgical risk; text searching; tool

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.

技术进步继续改善手术结果。但这些 技术也带来了新的挑战，如通信复杂性、设备 压力大，对手术团队成员的认知要求更高。在 换句话说，尽管技术有所改进，但手术仍将存在风险。有 有证据表明，可避免的并发症数量可能被低估，约39% 的院内不良事件与手术有关，多达4,000例手术从未发生过。 美国每年都发生一些不该发生的事情。 本研究的最终目标是开发一种高性能的自动检测系统（ADS）。 风险外科状态。ADS将通过以下方式在手术安全事件发生之前预防手术安全事件 实时监控并提前通知适当的手术室（OR）团队成员- 时间，如果有风险。从而让团队重新考虑下一步， 解决潜在问题，从而降低手术不良结局的发生率。 该项目证明了ADS基本组件的可行性和优点。 具体而言，手术安全性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指标，因此很可能 未来的ADS将通过跟踪血流中断来模拟和监控手术状态。我们目前 目标是（1）完成研究和探索性分析驱动的时间数据的实施 可视化（READ-TV）研究工具;可视化FD模式的开源软件和其他 纵向数据(2)开发一个随机模型来预测高风险、破坏性FD 序列将基于较早时间点的FD速率发生。(3)链路FD模式和 通过开发文本分类器来识别手术结果的序列， 根据相关EHR记录，发生手术安全事件或未遂事件。分类器 它将是一个深度学习模型，由成千上万的手术EHR笔记训练而成。 第三个目标中的文本分析将提供对FD类型和序列的洞察力， 容易出错，从而揭示了ADS应该警告OR团队避免的FD模式。 这种文本分析的其他好处包括可能确认 事故漏报 在完成这三个目标后，我们将拥有ADS的计算基础：我们的 研究工具（目标1：READ-TV可视化软件）和分析（目标3：链路流中断 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（目的2：根据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 通过研究流量中断的自动记录和适当的警报模式， 如果手术容易进入易出错的状态，