Safety Promotion through Early Event Detection in the Elderly (SPEEDe)

摘要 不良事件(AEs)--医疗护理对患者造成的伤害--影响多达 13.5%的住院患者；这些急性脑血管意外中有一半是可以预防的，急性脑血管意外尤其影响 老年人。众所周知，AES很难准确测量。各种纸张和电子产品 已经开发了触发工具来识别AEs；然而，它们的阳性预测值(PPV) 较低，需要后续耗时的手动图表检查才能准确测量AEs。 在拟议的项目中，我们将使用创新的、最先进的机器交互学习 (IML)技术，以提炼现有的声发射触发，大幅提高其准确性。我们会 我还开发了一款新型的AE Explorer，以加快对可能的AE的审查，以及一种创新的 一整套预测分析工具和测量和检测它们的方法。我们的方法 将专家驱动的改进与最新的IML技术相结合并进行比较 使触发器更准确，最终目标是创建准确的触发器 足以作为实际测量伤害的代替品。我们称我们的方法为安全 通过老年人早期事件检测(SPEEDe)进行推广。 我们的团队在机器学习、患者安全、风险管理、AE检测、 老年医学和触发工具专家将共同努力，实现这一具体目标 项目：(1)原型和快速迭代触发审查仪表板(不良事件 探索者)使用以用户为中心的设计流程，(2)开发和评估新颖的交互式 机器学习方法可实现更高效、更准确的不良事件图表审查和 触发改进，以及(3)将交互式机器学习整合到不良事件中 并在临床环境中对其进行前瞻性评估。

ABSTRACT Adverse events (AEs) – harm to patients that results from medical care – affect as many as 13.5% of hospitalized patients; half of these AEs are preventable and AEs particularly affect the elderly. AEs are notoriously difficult to measure accurately. A variety of paper and electronic trigger tools have been developed to identify AEs; however, their positive predictive value (PPV) is low, requiting subsequent, time-intensive manual chart review to accurately measure AEs. In the proposed project, we will use innovative, state-of-the-art machine interactive learning (IML) techniques to refine existing AE triggers, improving their accuracy substantially. We will also develop a novel AE Explorer to speed review of possible AEs, as well as an innovative package of predictive analytics tools and methods to measure and detect them. Our approach combines and compares expert-driven improvement with the most recent IML techniques to make triggers more accurate, with the ultimate goal of creating triggers that are accurate enough to stand in as proxies for actual measurement of harm. We call our approach Safety Promotion through Early Event Detection in the Elderly, or SPEEDe. Our team of accomplished machine learning, patient safety, risk management, AE detection, geriatric medicine and trigger tool experts will work together to carry out the specific aims of this project: (1) prototype and rapidly iterate a trigger review dashboard (the Adverse Event Explorer) using a user-centered design process, (2) develop and evaluate novel Interactive Machine Learning approaches for more efficient and accurate adverse event chart review and trigger refinement, and (3) Integrate Interactive Machine Learning into the Adverse Event Explorer and evaluate it prospectively in a clinical setting.