Using Modern Data Science Methods and Advanced Analytics to Improve the Efficiency, Reliability, and Timeliness of Cardiac Surgical Quality Data

使用现代数据科学方法和高级分析来提高心脏手术质量数据的效率、可靠性和及时性

• 批准号: 10542758
• 负责人: Nader Nabile Massarweh
• 金额: $ 67.72万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10542758
• 关键词: Address; American College of Surgeons; Automation; Awareness; Cardiac; Cardiac Surgery procedures; Caring; Clinical; Collection; Data; Data Analyses; Data Analytics; Data Collection; Data Element; Data Science; Databases; Early Diagnosis; Electronic Health Record; Evaluation; Excess Mortality; Feedback; Future; Goals; Hand; Hospitals; Industry; Infrastructure; Institution; Interview; Link; Manuals; Measurement; Methodology; Methods; Modeling; Modernization; Modification; National Heart, Lung, and Blood Institute; Natural Language Processing; Notification; Observational Study; Operative Surgical Procedures; Outcome; Patients; Performance; Perioperative Care; Private Sector; Process; Qualitative Methods; Registries; Reporting; Research; Research Personnel; Research Priority; Resources; Risk Adjustment; Safety; Societies; Statistical Computing; Structure; Sum; Thoracic Surgeon; Time; United States Department of Veterans Affairs; Update; Work; advanced analytics; data warehouse; experience; hospital performance; implementation strategy; improved; innovation; interest; mortality; novel; programs; response

Within existing national surgical quality improvement (QI) programs, there are numerous opportunities to improve the efficiency of data flow from the point of collection to the time at which performance-based feedback is provided to stakeholders. Current limitations of the QI data cycle include: (a) reliance on hand abstraction for data collection; (b) a retrospective and episodic (e.g.: quarterly, bi-annually, etc.) approach to analysis and feedback which creates a time lag from when the hospital’s performance is declining and when it is made aware; (c) small clusters of clinically meaningful poor performance may go of undetected using current episodic analytic structures. To address the first limitation, modern data science methods (MDSMs) could be used to automate the collection of some, or all, of the variables within surgical QI registries. Full or partial automation of data collection could allow the substantial resources currently committed to manual data abstraction to be repurposed to support more continuous, proactive engagement in local QI activities. To address the limitations associated with episodic performance evaluation, alternative approaches for analyzing data in more real-time could be applied to provide an early warning of declining performance. The Veterans Affairs (VA) Surgical Quality Improvement Program (VASQIP) is one of the most successful and longest- standing national clinical registries used for surgical QI and has been the template for a number of similar programs in the private sector. As such, VASQIP represents an excellent model for evaluating alternative approaches to data collection and analysis that could allow for more efficient data flow through the quality improvement cycle and enhance national surgical QI efforts. The overall goal of this proposal is to evaluate alternative, potentially more efficient strategies that can be readily implemented within the existing infrastructure of contemporary surgical QI programs and aid in the more efficient flow of data. The specific aims are to: (1) develop and validate MDSMs to use structured and unstructured electronic health record data to automate cardiac VASQIP data collection; (2) compare the risk-adjusted CUSUM (a statistical process control methodology borrowed from industry) to quarterly observed-to-expected ratios (i.e.: VASQIP’s current approach to assessing performance) for evaluating VA hospital cardiac surgical performance; (3) conduct semi- structured interviews with diverse stakeholder groups to set a national research agenda for expansion and improvement of surgical QI programs. This mixed-methods proposal will involve observational studies using VASQIP and VA Corporate Data Warehouse data for patients who underwent cardiac surgery at a VA hospital between 2016 and 2020 as well as qualitative interviews with stakeholders who can help to inform future changes that can improve the data available within VASQIP. This project is important and novel because it will provide real-world, generalizable data that can be used to inform national surgical and non-surgical QI initiatives within VA and the private sector.

在现有的国家外科质量改进 (QI) 计划中，有很多机会 提高从收集点到基于性能的数据流的效率 向利益相关者提供反馈。目前 QI 数据周期的局限性包括： (a) 对现有数据的依赖。 数据收集的摘要；(b) 采用回顾性和偶发性（例如：每季度、每半年等）的方法 分析和反馈导致医院绩效下降和绩效改善存在时间滞后 (c) 使用电流可能无法检测到一小群具有临床意义的不良表现 为了解决第一个限制，现代数据科学方法（MDSM）可以是。 用于自动收集外科 QI 注册表中的部分或全部变量。 数据收集的自动化可以使目前用于手动数据的大量资源得到利用 抽象将被重新调整用途，以支持更持续、更主动地参与本地质量保证活动。 解决与情景绩效评估、替代分析方法相关的局限性 更实时的数据可以用于为退伍军人的表现提供预警。 事务 (VA) 手术质量改进计划 (VASQIP) 是最成功、持续时间最长的计划之一 用于外科 QI 的常设国家临床注册中心，并已成为许多类似的模板 因此，VASQIP 是评估替代方案的绝佳模型。 数据收集和分析的方法可以通过质量保证更有效的数据流 改进周期并加强国家外科 QI 工作 该提案的总体目标是评估。 替代的、可能更有效的策略，可以在现有的框架内轻松实施 当代外科 QI 项目的基础设施，并有助于更有效的数据流。 目标是：(1) 开发和验证 MDSM 以使用结构化和非结构化电子健康记录数据 自动收集心脏 VASQIP 数据；(2) 比较风险调整 CUSUM（统计过程） 从行业借用的控制方法）到季度观察到预期比率（即：VASQIP当前的 (3) 进行半 与不同利益相关者群体进行结构化访谈，以制定扩大和扩展的国家研究议程 该混合方法提案将涉及使用的观察性研究。 VASQIP 和 VA Corporate Data Warehouse 数据，提供在 VA 医院接受心脏手术的患者的数据 2016 年至 2020 年间，以及对利益相关者进行定性访谈，这些访谈有助于为未来提供信息 可以改善 VASQIP 内可用数据的更改该项目很重要且新颖，因为它将。 提供真实世界的、可概括的数据，可用于告知国家外科和非外科质量状况 VA 和私营部门内的举措。