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

我们如何才能使侵入性非手术程序更安全？

• 批准号: 10399528
• 负责人: Hillary Jane Mull
• 金额: --
• 依托单位: VA BOSTON HEALTH CARE SYSTEM
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10399528
• 关键词: Acute Renal Failure with Renal Papillary Necrosis; Address; Adherence; Admission activity; Adverse event; Ambulatory Surgical Procedures; Anesthesia procedures; Antibiotics; Area; Big Data; Cardiology; Caring; Cessation of life; Characteristics; Clinic; Clinical; Clostridium difficile; Communities; Country; Data; Data Set; Databases; Detection; Devices; Disparity; Doctor of Philosophy; Emergency department visit; Evaluation; Event; Fostering; Frequencies; Frontline worker; Funding; Gastroenterology; Gastrointestinal Endoscopy; Guidelines; Health Services Research; Healthcare; Homogeneously Staining Region; Hospitalization; Infection; Informatics; Infrastructure; Injections; Intervention; Interventional radiology; Investigator-Initiated Research; K-Series Research Career Programs; Link; Literature; Logistic Regressions; Measures; Medical Informatics; Methodology; Methods; Modeling; Operating Rooms; Operative Surgical Procedures; Patient Care; Patient-Focused Outcomes; Patients; Performance; Pharmaceutical Preparations; Policies; Predictive Value; Probability; Procedures; Process; Provider; Radiology Specialty; Research; Research Personnel; Research Project Grants; Risk Adjustment; Risk Factors; Sampling; Site; Sterility; Structure; Surgical incisions; Surveillance Modeling; System; Testing; Text; Training; Update; Variant; Work; adverse event monitoring; adverse outcome; career; case-based; clinical care; data mining; data warehouse; design; experience; health care disparity; health care service utilization; improved; informatics tool; innovation; medical specialties; neglect; novel; patient safety; pharmacy benefit; predictive modeling; prevent; racial disparity; respiratory; safe patient; structured data; surveillance data; tool; wound

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.

背景：这是第二次提交 HSR&D IIR 提案，以过渡 Hillary Mull 博士。 从她的 HSR&D 职业发展奖 (CDA) 项目转向独立的 VA 医疗服务 研究生涯。拟议的工作旨在通过改编 Mull 博士成功的 CDA 项目来建立她的 开发和验证门诊手术和侵入性手术监测模型的方法 非外科临床专科：介入心脏病学、介入放射学、胃肠病学 内窥镜检查程序。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起来 VA 企业数据仓库 (CDW)。 Mull 博士的 CDA 资助的监测研究发现了一个不利因素 事件发生率为 9%，阳性预测值为 85%，显着改善了不良事件检测。 意义/影响：目前，尚无对侵入性操作的主动监测和初步的监测。 与一线工作人员的分析和对话表明，不良事件发生的频率较高，并且会造成不良事件的发生 严重的患者伤害。先前的研究发现这三个专业的侵入性操作会导致术后 每年急诊室就诊或住院治疗的病例数超过 50,000 例。非 VA 文献 表明这种利用的一半可以通过改善临床护理（例如，坚持 抗生素处方指南）。随着护理的日益增多，这一研究领域将变得更加重要 在手术室外进行转换。使用以下方法检测和监测未充分研究环境中的不良事件 VA CDW 中的现有数据与 HSR&D 资助优先事项 C-医疗保健信息学一致。 创新：与全国COIN的专家一起以及运营伙伴的支持 来自每个临床专业、药房福利管理和 VA 信息学和计算 基础设施方面，Mull 博士建议运用她的 CDA 专业知识来建立一个监测系统来识别侵入性 具有可预防不良事件的非手术程序；这些程序不受任何 VA 的约束 监视活动。这项工作解决的第二个差距是缺乏全国可用的数据集捕获 手术麻醉的使用。我们将使用图表审查和文本查询数据挖掘方法来获取此信息 信息。我们 IIR 工作的巅峰将是一个关于不良事件和不良事件的综合数据库。 VA 研究人员可以获得潜在可修改的影响因素，包括手术麻醉数据。 具体目标： 1) 使用 2017-20 财年数据开发和验证监测模型； 2）测试监控 从 21-22 财年开始，系统（应用模型系数，每月进行有限的图表审查），并完善 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 是否涉及训练有素的麻醉人员或患者是否接受了不适当的抗生素。 方法：我们的样本包括由临床专家共同定义的非手术侵入性程序 调查人员。我们将遵循 Mull 博士的 CDA 工作中概述的方法来汇总患者、程序、 来自 CDW 的提供商和设施数据。接下来，我们将审查案例以确定是否是可预防的事件 发生并使用图表审查数据来估计逻辑回归模型，预测发生的可能性 不良事件。模型系数将持续应用，以识别可能存在风险的案例 不良事件目标图表审查。监测数据将用于检验研究假设。 后续步骤/实施：通过本 IIR 以及随后的合作伙伴资助的 QUERI 提案，Dr. Mull 和她的团队将建立一个专为侵入性非手术治疗而设计的不良事件监测系统 可用于评估可修改的护理流程以防止患者受到伤害的程序。通过识别风险 对于可预防的不良事件的因素，我们可以与我们的运营合作伙伴一起确定应该在哪些方面 重点关注 QUERI 资​​助的 QI 工作以改善患者安全。研究结果将提供急需的信息 研究和临床社区继续衡量和提高 VA 护理质量。