Enhancing the Efficiency of Data Collection for Surgical Quality Improvement

提高数据收集效率以提高手术质量

• 批准号: 10187843
• 负责人: Nader Nabile Massarweh
• 金额: --
• 依托单位: MICHAEL E DEBAKEY VA MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10187843
• 关键词: Address; Affect; Caring; Clinical; Collection; Data; Data Collection; Data Sources; Electronic Health Record; Ensure; Evaluation; Goals; Hand; Hospital Mortality; Hospitals; Human Resources; Hybrids; Infrastructure; Institution; Interview; Link; Manuals; Measures; Methodology; Methods; Mind; Modeling; Monitor; Morbidity - disease rate; Nurses; Observational Study; Occupations; Office Surgery; Operative Surgical Procedures; Outcome; Patients; Performance; Perioperative; Postoperative Complications; Postoperative Period; Predictive Value; Private Sector; Provider; Quality Indicator; Registries; Reporting; Research; Resources; Risk; Safety; Sampling; Structure; Time; Training; United States Department of Veterans Affairs; base; comparative effectiveness; cost; data access; data registry; data warehouse; expectation; hospital performance; improved; informant; innovation; mortality; novel; perioperative morbidity; perioperative mortality; practice setting; programs; prospective; success

Background: Although the majority of national quality initiatives utilize electronic health record (EHR) or administrative data, their ability to adequately discriminate performance has been brought into question and it is unclear certain outcomes, such as postoperative complications, are accurately ascertained. By comparison, clinical registry data, like the VA Surgical Quality Improvement Program (VASQIP), are widely considered robust for performance evaluation and quality improvement (QI). But, VASQIP data collection is resource intensive—data are manually abstracted by trained local Surgical Quality Nurses (SQNs) for a systematic sample of surgical cases performed at all VA hospitals. VASQIP then uses the data to characterize the quality and safety of surgical care at each hospital based on risk-adjusted 30-day morbidity and mortality rates. Significance: VASQIP data collection practices present two important limitations. First, perioperative outcome rates have significantly decreased the past two decades making it unclear whether systematic case sampling is adequately powered to identify underperforming hospitals. Second, the time required for VASQIP data collection detracts from SQNs’ ability to engage in other important job functions, like local QI activities. Because SQNs spend substantial time working with VASQIP data, this represents an important missed opportunity to identify a quality problem when it is evolving rather than when it has already occurred. As such, alternative approaches that can provide reliable data and decrease the burden of data collection would have tangible benefits for other national surgical and non-surgical QI initiatives within VA and the private sector. Innovation: This project is novel because it can change the paradigm regarding the collection of QI data from purely EHR or clinical registry to a more efficient hybrid model that could address reliability concerns associated with the use of EHR (or administrative) data alone. It will also provide real-world, generalizable data that can only be obtained within VA's data platform and can inform VA and the private sector national surgical and non-surgical QI initiatives. We have two national operational partners: 1.) VA National Surgery Office (NSO); 2.) Office of Reporting, Analytics, Performance, Improvement, and Deployment (RAPID). Specific Aims: The overall goal is to address two important questions. First, given low perioperative outcome rates across VA, is systematic sampling robust enough to inform surgical QI? Second, are hybrid data (i.e.: EHR combined with clinical registry variables) a potentially reliable alternative for measuring VA hospital surgical performance? These questions will be explored through the following specific aims: (1) Evaluate whether analyzing all VASQIP-eligible surgical cases, relative to current systematic case sampling, improves negative predictive value (i.e.: decreases false negative rates) for identifying VA hospitals with outlier performance; (2) Compare the use of hybrid EHR and clinical registry data, relative to clinical registry alone, for evaluating risk-adjusted surgical performance at VA hospitals; (3) Explore how more efficient VASQIP data collection could enhance local QI efforts through in-depth, key informant interviews with SQNs. Methodology: This mixed-methods proposal will involve hospital-level, observational studies using VASQIP and Corporate Data Warehouse (CDW) data from patients who underwent non-cardiac surgery (2016-2019) as well as qualitative interviews with SQNs. With comparative effectiveness in mind, these data will be used to explore what would be observed if data from all surgical cases were included in VASQIP and to understand whether other existing VA data sources might improve VASQIP data collection efficiency and enhance local QI. Next Steps: With the NSO, we will prospectively compare the fidelity of hand-abstracted variables to automatable variables from CDW. The implementation plan (supported by the VA National Director of Surgery) will utilize VASQIP’s existing infrastructure by partnering with VINCI to provide the NSO with centralized CDW access (using RAPID’s data access model as a template) allowing automated data collection.

背景：尽管大多数国家质量倡议利用电子健康记录（EHR）或 行政数据，他们的能力，充分区分业绩已受到质疑，它 不清楚某些结果，如术后并发症，是准确确定的。相比之下， 临床注册数据，如VA外科质量改进计划（VASQIP），被广泛认为是 强大的性能评估和质量改进（QI）。但是，VASQIP数据收集是资源 由经过培训的当地外科质量护士（SQN）手动提取重症数据， 在所有VA医院进行的手术病例样本。VASQIP然后使用数据来表征质量 根据风险调整后的30天发病率和死亡率，评估每家医院的手术治疗安全性。 重要性：VASQIP数据收集实践存在两个重要的局限性。一、围手术期 在过去的二十年里，结果率显著下降，这使得不清楚系统性病例是否 抽样有足够的力量来确定表现不佳的医院。二、VASQIP所需时间 数据收集削弱了SQN参与其他重要工作职能（如本地QI活动）的能力。 由于SQN花费大量时间处理VASQIP数据，因此这代表了一个重要的遗漏 当质量问题正在发展而不是已经发生时，有机会识别质量问题。因此，在本发明的一个方面， 能够提供可靠数据和减轻数据收集负担的替代办法将 为VA和私营部门内的其他国家手术和非手术QI计划带来实实在在的好处。 创新：该项目是新颖的，因为它可以改变关于QI数据收集的范式， 从纯粹的EHR或临床注册到可以解决可靠性问题的更有效的混合模型 仅使用EHR（或行政）数据。它还将提供真实世界的，可推广的 只能在VA的数据平台内获得的数据，可以告知VA和私营部门的国家 手术和非手术QI计划。我们有两个国家业务伙伴：1。弗吉尼亚州国家外科 （2.）报告、分析、性能、改进和部署办公室（RAPID）。 具体目标：总体目标是解决两个重要问题。首先，鉴于低围手术期 VA的结局率，系统抽样是否足够可靠以告知手术QI？第二，混合数据 (i.e.: EHR结合临床登记变量）是测量VA的潜在可靠替代方法 医院手术表现？将通过以下具体目标探讨这些问题：（1） 评价是否分析了所有符合VASQIP条件的手术病例，相对于当前系统性病例采样， 提高阴性预测值（即：降低假阴性率）用于识别具有离群值的VA医院 性能;（2）比较混合EHR和临床注册数据的使用，相对于单独的临床注册， 用于评估VA医院风险调整后的手术性能;（3）探索如何更有效地VASQIP数据 收集可以通过与SQN进行深入的关键线人访谈来加强当地QI工作。 方法学：该混合方法提案将涉及使用VASQIP的医院级观察性研究 和接受非心脏手术的患者的企业数据仓库（CDW）数据（2016-2019年）， 以及与SQN的定性访谈。考虑到相对有效性，这些数据将用于 探索如果所有手术病例的数据都纳入VASQIP中，将观察到什么，并了解 其他现有的VA数据源是否可以提高VASQIP数据收集效率并增强本地QI。 下一步：使用NSO，我们将前瞻性地比较手工抽象变量的保真度， 来自CDW的自动化变量。执行计划（得到退伍军人事务部国家主任的支持） 外科）将通过与芬奇合作，利用VASQIP现有的基础设施， 集中式CDW访问（使用RAPID的数据访问模型作为模板），允许自动化数据收集。