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: 10641658
• 负责人: Nader Nabile Massarweh
• 金额: --
• 依托单位: VETERANS HEALTH ADMINISTRATION
• 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/10641658
• 关键词: Address; Affect; Caring; Clinical; Collection; Data; Data Adjustments; Data Collection; Data Sources; Electronic Health Record; Eligibility Determination; 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 Adjustment; Safety; Sampling; Structure; Time; Training; United States Department of Veterans Affairs; 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计划都利用电子健康记录（EHR）或 行政数据，他们充分区分绩效的能力已受到质疑， 不清楚某些结果，例如术后并发症，可以准确确定。相比之下， 临床注册表数据，例如VA手术质量改进计划（VASQIP），被广泛考虑 绩效评估和质量改进（QI）的强大功能。但是，VASQIP数据收集是资源 密集型 - DATA是由训练有素的当地手术质量护士（SQN）手动抽象的 在所有VA医院进行的手术病例样本。然后，vasqip使用数据来表征质量 基于风险调整后的30天发病率和死亡率，每家医院的手术护理安全。 意义：VASQIP数据收集实践提出了两个重要的局限性。首先，周期性 结果率显着降低了过去二十年 采样充分有助于确定表现不佳的医院。第二，vasqip所需的时间 数据收集损害了SQN从事其他重要工作功能（例如本地QI活动）的能力。 由于SQN花费大量时间来处理VASQIP数据，这代表了一个重要的错过 当质量问题发展而不是已经发生时，有机会识别质量问题。像这样， 可以提供可靠数据并减少数据收集燃烧的替代方法将具有 在弗吉尼亚州和私营部门内的其他国家手术和非手术QI计划的切实福利。 创新：这个项目是新颖的，因为它可以改变有关从中收集Qi数据的范式 纯粹是EHR或临床注册表，用于更有效的混合模型，该模型可以解决可靠性问题 与仅使用EHR（或管理）数据相关联。它还将提供现实世界中的可推广 只能在VA的数据平台中获得的数据，并且可以通知VA和私营部门国家 手术和非手术Qi计划。我们有两个国家运营伙伴：1。）VA国家手术 办公室（NSO）； 2.）报告，分析，绩效，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，给定低时期 VA的结果率，系统的采样功能是否足以告知手术气？第二，是混合数据 （即：EHR与临床注册表变量相结合）测量VA的潜在可靠替代方案 医院手术表现？这些问题将通过以下特定目的进行探讨：（1） 评估是否分析了所有相对于当前系统病例采样的所有VASQQIP符合手术病例的分析 提高负面预测价值（即降低假负率），以识别有离群的VA医院 表现; （2）比较单独相对于临床注册表的混合EHR和临床注册表数据的使用 用于评估VA医院的风险调整外科手术表现； （3）探索如何更有效的VASQIP数据 收集可以通过对SQNS的深入，关键的线人访谈来增强本地质量检查的努力。 方法论：该混合方法提案将涉及使用VASQIP的医院级别的观察性研究 以及接受非心脏外科手术（2016- 2019年）的患者的公司数据仓库（CDW）数据 以及对SQNS的定性访谈。考虑到比较有效性，这些数据将用于 探索如果所有外科手术病例的数据都包含在Vasqip中并了解 其他现有的VA数据源是否可以提高VASQIP数据收集效率并提高本地气质。 下一步：使用NSO，我们可能会比较手工吸收变量的保真度 CDW的自动变量。实施计划（由VA国家主任的支持 手术）将通过与Vinci合作提供NSO，利用Vasqip的现有基础设施 集中式CDW访问（使用Rapid的数据访问模型作为模板）允许自动数据收集。

项目成果

Case Sampling vs Universal Review for Evaluating Hospital Postoperative Mortality in US Surgical Quality Improvement Programs.

美国手术质量改进计划中评估医院术后死亡率的病例抽样与普遍审查。

• DOI: 10.1001/jamasurg.2023.4532
• 发表时间: 2023
• 期刊: JAMA surgery
• 影响因子: 16.9
• 作者: Chen,ViviW;Chidi,AlexisP;Rosen,Tracey;Dong,Yongquan;Richardson,PeterA;Kramer,Jennifer;Axelrod,DavidA;Petersen,LauraA;Massarweh,NaderN
• 通讯作者: Massarweh,NaderN