How Can We Make Invasive Non-Surgical Procedures Safer? Using Big Data to Identify Adverse Events and Opportunities to Mitigate Harm

背景：这是第二次提交HSR&D IIR提案，以过渡Hillary穆尔博士。 从她的HSR&D职业发展奖（CDA）项目转向独立的VA健康服务 研究生涯。拟议的工作旨在建立在穆尔博士的成功的CDA项目，通过调整她的 开发和验证门诊手术到侵入性手术的监测模型的方法 非手术临床专业：介入心脏病学、介入放射学和胃肠道 内窥镜检查。这种基于信息学的方法依赖于将文本和结构化数据字段组合在一起， 企业数据仓库（CDW）穆尔博士的CDA资助的监测研究发现， 事件发生率为9%，阳性预测值为85%，显著提高了不良事件检测。 意义/影响：目前，没有积极监测侵入性手术和初步 分析和与一线工作人员的交谈表明，不良事件发生频率较高， 严重患者伤害。先前的工作发现，在这三个专业的侵入性程序导致后， 每年超过50，000例急诊或住院治疗。非VA文献 表明通过临床护理的改善，这种利用的一半是可以预防的（例如，遵守 抗生素处方指南）。这一研究领域将变得更加重要， 手术室外的过渡在未充分研究的环境中检测和监测不良事件， VA CDW中的现有数据与HSR&D资助优先级C-Healthcare Informatics一致。 创新：与来自全国各地的COIN专家以及运营合作伙伴的支持一起 来自每个临床专业，药房福利管理和VA信息学和计算 基础设施，穆尔博士建议运用她的CDA专业知识，建立一个监测系统，以确定入侵 发生可预防不良事件的非外科手术;这些手术不受任何VA影响 监视活动。这项工作解决的第二个差距是缺乏一个国家可用的数据集， 程序麻醉使用。我们将使用图表审查和文本查询数据挖掘方法来获得这一点 信息.我们的IIR工作的高潮将是一个全面的不良事件数据库， 潜在可修改的影响因素，包括VA研究人员可用的手术麻醉数据。 具体目标：1）使用FY 17 -20数据开发和验证监测模型; 2）测试监测 系统（应用模型系数，每月进行有限的图表审查），并进行改进 该系统使用额外的CDW变量; 3）测试与可修改过程相关的假设，包括 是否有受过训练的麻醉提供者参与，或患者接受了不适当的抗生素。 方法：我们的样本包括由专家临床医生共同定义的非手术侵入性程序， investigators.我们将遵循穆尔博士CDA工作中概述的方法，将患者、手术 来自CDW的供应商和设施数据。接下来，我们将审查案例，以确定是否可以预防的事件 发生并使用图表审查数据来估计逻辑回归模型， 不良事件。将持续应用模型系数，以确定可能发生 不良事件至目标图表审查。监测数据将用于检验研究假设。 下一步/实施：通过这个IIR，并在随后的合作伙伴资助的QUERI建议，博士。 穆尔和她的团队将建立一个不良事件监测系统， 可用于评估可修改的护理过程以防止患者伤害的程序。通过识别风险 可预防的不良事件的因素，我们可以确定我们应该与我们的运营合作伙伴， 关注QUERI资助的QI工作，以提高患者安全。研究结果将提供急需的信息， 研究和临床社区，因为他们继续衡量和提高VA护理的质量。

Background: This is the second submission of an HSR&D IIR proposal to transition Dr. Hillary Mull, Ph.D. from her HSR&D Career Development Award (CDA) project toward an independent VA health services research career. The proposed work seeks to build on Dr. Mull's successful CDA project by adapting her approach to developing and validating a surveillance model for outpatient surgery to invasive procedures in non-surgical clinical specialties: interventional cardiology, interventional radiology and gastrointestinal endoscopy procedures. This informatics-based approach relies on combining text and structured data fields in the VA Corporate Data Warehouse (CDW). Dr. Mull's CDA-funded surveillance research identified an adverse event rate of 9% and had a positive predictive value of 85%, dramatically improving adverse event detection. Significance/Impact: Presently, there is no active surveillance of invasive procedures and preliminary analyses and conversations with frontline staff suggest adverse events occur with some frequency and impose significant patient harm. Prior work found invasive procedures in these three specialties result in post- procedure emergency room visits or hospitalizations exceeding 50,000 cases annually. Non-VA literature suggests half of this utilization may be preventable with improvements in clinical care (e.g., adherence to antibiotic prescribing guidelines). This field of research will become even more important as care increasingly transitions outside the operating room. Detecting and monitoring adverse events in understudied settings using existing data in the VA CDW is consistent with HSR&D funding priority C-Healthcare Informatics. Innovation: Together with experts from COINs around the country and the support of operational partners from each clinical specialty, Pharmacy Benefits Management and VA Informatics and Computing Infrastructure, Dr. Mull proposes to apply her CDA expertise to build a surveillance system to identify invasive non-surgical procedures with preventable adverse events; these procedures are not subject to any VA surveillance activities. A second gap this work addresses is the lack of a nationally available dataset capturing procedural anesthesia use. We will use chart review and text-query data mining methods to obtain this information. The culmination of our IIR work will be a comprehensive database of adverse events and potentially modifiable contributing factors, including procedural anesthesia data, available to VA researchers. Specific Aims: 1) develop and validate surveillance models using FY17-20 data; 2) test the surveillance system (apply model coefficients, perform limited chart review on a monthly basis) from FY21-22, and refine the system using additional CDW variables; 3) test hypotheses related to modifiable processes including whether a trained anesthesia provider was involved or patients received inappropriate antibiotics. Methodology: Our sample includes non-surgical invasive procedures defined by expert clinician co- investigators. We will follow the methods outlined in Dr. Mull's CDA work to aggregate patient, procedure, provider and facility data from the CDW. Next, we will review cases to determine whether a preventable event occurred and use chart review data to estimate logistic regression models predicting the likelihood of an adverse event. Model coefficients will be applied on an ongoing fashion to identify cases likely to have an adverse event to target chart review. Surveillance data will be used to test study hypotheses. Next Steps/Implementation: Through this IIR, and in a subsequent partner-funded QUERI proposal, Dr. Mull and her team will establish an adverse event surveillance system designed for invasive non-surgical procedures that can be used to assess modifiable processes of care to prevent patient harm. By identifying risk factors for preventable adverse events, we can determine where we, with our operational partners, should focus QUERI-funded QI work to improve patient safety. Study results will provide much needed information to the research and clinical communities as they continue to measure and improve the quality of VA care.