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.

在现有的国家外科手术质量改进(QI)计划中，有许多机会 提高从采集点到基于性能的时间点的数据流的效率 向利益相关者提供反馈。目前QI数据周期的局限性包括：(A)依赖手头 数据收集的摘要；(B)回顾和间歇性(例如：季度、半年等)接近 分析和反馈，这会造成医院业绩下降和下降的时间滞后 已知晓；(C)具有临床意义的性能较差的小群集可能未被检测到 情节分析结构。为了解决第一个限制，现代数据科学方法(MDSM)可以 用于自动收集外科QI注册表中的部分或全部变量。全部或部分 数据收集的自动化可以使目前用于手动数据的大量资源 重新调整抽象的用途，以支持更持续、更主动地参与当地的QI活动。至 解决与时断时续的绩效评估、分析的替代方法相关的限制 可以应用更实时的数据来提供性能下降的早期预警。退伍军人 退伍军人事务部(VA)外科质量改进计划(VASQIP)是最成功、持续时间最长的- 用于外科QI的常备国家临床注册，并已成为许多类似的 私营部门的项目。因此，VASQIP代表了评估替代方案的优秀模型 数据收集和分析的方法，可使数据在质量中更有效地流动 改善周期，加强全国外科QI工作。这项提案的总体目标是评估 替代的、可能更有效的战略，可以在现有的 现代外科QI计划的基础设施，并有助于更有效的数据流。具体的 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 使心脏VASQIP数据收集自动化；(2)比较风险调整的累积和(统计过程 从行业借用的控制方法)到季度观察与预期比率(即：VASQIP的当前 绩效评估方法)用于评价退伍军人医院心脏手术的绩效；(3)进行半 与不同的利益相关者群体进行结构化访谈，以制定国家研究议程，以扩大和 改进外科QI计划。这项混合方法的提案将涉及使用 退伍军人医院心脏手术患者的VASQIP和退伍军人企业数据仓库数据 2016至2020年，以及对可帮助了解未来情况的利益相关者进行定性访谈 可以改善VASQIP中可用数据的更改。这个项目很重要，也很新颖，因为它将 提供可用于向全国外科和非外科QI提供信息的真实、通用的数据 退伍军人事务部和私营部门的倡议。