Enhancing the Efficiency of Data Collection for Surgical Quality Improvement

背景：尽管大多数国家质量倡议使用电子健康记录(EHR)或 管理数据，他们充分区分业绩的能力受到了质疑，而且它 尚不清楚某些结果，如术后并发症，可以准确确定。相比之下， 临床登记数据，如退伍军人外科质量改进计划(VASQIP)，被广泛考虑 稳健地进行绩效评估和质量改进(QI)。但是，VASQIP数据收集是一种资源 密集的数据由训练有素的当地外科素质护士(SQN)手动提取，以进行系统的 在所有退伍军人医院进行的手术病例样本。然后，VASQIP使用这些数据来表征质量 根据风险调整后的30天发病率和死亡率，每家医院的外科护理的安全性。 意义：VASQIP数据收集做法存在两个重要限制。第一，围手术期 在过去的二十年中，结果发生率显著下降，因此尚不清楚系统性病例 抽样有足够的能力来识别表现不佳的医院。第二，VASQIP所需的时间 数据收集削弱了SQN参与其他重要工作职能的能力，如本地QI活动。 由于Sqns花费大量时间处理VASQIP数据，这代表着一个重要的遗漏 在质量问题发展过程中而不是已经发生时发现问题的机会。因此， 可以提供可靠数据并减轻数据收集负担的替代方法将具有 为退伍军人事务部和私营部门内的其他国家外科和非外科QI倡议带来切实的好处。 创新：这个项目很新颖，因为它可以改变关于收集QI数据的范式 将纯粹的电子病历或临床登记转变为更高效的混合模式，可以解决可靠性问题 仅与使用电子病历(或管理)数据相关联。它还将提供真实世界的、可推广的 数据只能在退伍军人管理局的数据平台内获得，并可以通知退伍军人管理局和国家私营部门 外科和非外科QI倡议。我们有两个国家业务伙伴：1.)退伍军人事务部国家外科 办公室(NSO)；2.报告、分析、性能、改进和部署办公室(快速)。 具体目标：总体目标是解决两个重要问题。首先，考虑到围手术期较低 整个退伍军人管理局的结果比率，系统抽样是否足够稳健，以告知外科QI？第二，是混合数据吗 (即：EHR结合临床登记变量)是测量VA的潜在可靠替代方案 医院的手术表现如何？这些问题将通过以下具体目标进行探讨：(1) 评估是否分析所有符合VASQIP条件的手术病例，相对于当前系统的病例抽样， 提高识别退伍军人医院异常值的阴性预测值(即：降低假阴性率) 性能；(2)比较混合EHR和临床登记数据的使用，相对于单独的临床登记， 用于评估退伍军人医院经风险调整的手术绩效；(3)探索如何更有效地使用VASQIP数据 收集可以通过对SQN的深入、关键的线人访谈来加强当地的QI努力。 方法：这项混合方法的建议将涉及使用VASQIP的医院水平的观察性研究 和企业数据仓库(CDW)来自接受非心脏手术的患者(2016-2019年)的数据，如 以及对Sqns的定性采访。考虑到相对有效性，这些数据将被用于 探索如果所有手术病例的数据都包含在VASQIP中，将会观察到什么，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的自动变量。执行计划(由退伍军人事务部国家主任支持 Surgery)将利用VASQIP的现有基础设施，与芬奇合作，为NSO提供 集中式CDW访问(使用RAPID的数据访问模型作为模板)，允许自动数据收集。

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.