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举措都利用电子健康记录 (EHR) 或 行政数据，其充分区分绩效的能力受到质疑，并且 通过比较，可以准确确定某些不清楚的结果，例如术后并发症。 临床注册数据，如 VA 手术质量改进计划 (VASQIP)，被广泛考虑 对于绩效评估和质量改进 (QI) 来说是稳健的但是，VASQIP 数据收集是资源。 密集型——数据由经过培训的当地外科优质护士 (SQN) 手动提取，用于系统化分析 然后使用所有 VA 医院进行的手术病例样本来描述质量。 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。 意义：VASQIP 数据收集实践存在两个重要的局限性：首先，围手术期。 过去二十年来，结果率显着下降，因此尚不清楚系统性病例是否 抽样有足够的能力来识别表现不佳的医院。 其次，VASQIP 所需的时间。 数据收集会降低 SQN 参与其他重要工作职能（例如当地 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 有机会在质量问题正在发展时而不是已经发生时识别它。 可以提供可靠数据并减轻数据收集负担的替代方法 为 VA 和私营部门内的其他国家外科和非外科 QI 举措带来切实的好处。 创新：这个项目很新颖，因为它可以改变收集 QI 数据的范式 纯粹的电子病历或临床登记到更有效的混合模型，可以解决可靠性问题 与单独使用 EHR（或管理）数据相关的数据还将提供真实世界的、可概括的。 数据只能在 VA 的数据平台内获取，并且可以通知 VA 和私营部门国家 我们有两个国家运营合作伙伴： 1.) VA 国家外科手术。 办公室 (NSO)；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，首先是围手术期较低。 VA 的结果率，系统抽样是否足够强大，足以为手术 QI 提供信息？ 其次，混合数据是否存在？ （即：EHR 与临床登记变量相结合）测量 VA 的潜在可靠替代方案 医院手术表现如何？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 资格的手术病例（相对于当前的系统病例抽样） 提高识别异常值 VA 医院的阴性预测值（即：降低假阴性率） (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数据， 用于评估 VA 医院的风险调整手术表现；(3) 探索如何更有效地使用 VASQIP 数据 通过与 SQN 进行深入、关键的知情人访谈，收集数据可以加强当地的 QI 工作。 方法：该混合方法提案将涉及使用 VASQIP 的医院级别观察性研究 以及接受非心脏手术的患者的企业数据仓库 (CDW) 数据（2016-2019 年） 以及对 SQN 的定性访谈，考虑到比较有效性，这些数据将用于 探索如果所有手术病例的数据都包含在 VASQIP 中会观察到什么，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。 后续步骤：通过 NSO，我们将前瞻性地将手工抽象变量的保真度与 CDW 的自动化变量 实施计划（由 VA 国家主任支持） 手术）将与 VINCI 合作，利用 VASQIP 的现有基础设施，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。