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.

在现有的国家手术质量改进（QI）计划中，有许多机会， 提高从收集点到基于性能的 向利益攸关方提供反馈。QI数据周期目前的局限性包括：（a）依赖现有数据 数据收集的摘要;（B）回顾性和偶发性（例如：每季度、每半年等）方法 分析和反馈，从医院绩效下降到 （c）使用当前的技术，可能无法检测到具有临床意义的小簇不良表现 情节分析结构。为了解决第一个限制，可以使用现代数据科学方法（MDSM）。 用于自动收集外科QI登记系统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工数据的大量资源 抽象将被重新利用，以支持更持续、更主动地参与当地QI活动。到 解决与情景绩效评估相关的局限性，分析的替代方法 可以应用更实时的数据，对业绩下降提出预警。退伍军人 事务（VA）外科质量改进计划（VASQIP）是最成功和最长的- 用于外科QI的常设国家临床登记中心，并已成为许多类似的模板 私营部门的方案。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，可以通过质量保证提高数据流的效率， 改进周期和加强国家外科质量保证工作。本提案的总体目标是评估 替代的，可能更有效的战略，可以很容易地在现有的 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 目标是：（1）开发和验证MDSM，以使用结构化和非结构化的电子健康记录数据 自动化心脏VASQIP数据收集;（2）比较风险调整后的VASQIP（统计过程 从工业界借鉴的控制方法）到季度预测与预期比率（即：VASQIP电流 评估性能的方法），用于评估VA医院心脏手术性能;（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， 改进外科质量保证计划。这种混合方法的建议将涉及观察性研究， 在VA医院接受心脏手术的患者的VASQIP和VA公司数据仓库数据 2016年至2020年之间，以及与利益相关者的定性访谈，这些利益相关者可以帮助为未来提供信息。 可以改善VASQIP中可用数据的更改。这个项目是重要的和新颖的，因为它将 提供可用于告知国家手术和非手术QI的真实、可推广的数据 在私营部门和私营部门的活动。