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，有很多机会 提高数据流的效率从收集点到基于绩效的时间 向利益相关者提供反馈。 QI数据周期的当前局限性包括：（a）手头保留 数据收集的抽象； （b）回顾性和情节（例如：季度，一年一度等） 分析和反馈会产生时间滞后，从医院的表现下降到何时何时 意识到； （c）使用电流的临床上有意义的较差差差差异可能未被发现 情节分析结构。为了解决第一个限制，现代数据科学方法（MDSM）可能是 用于自动化手术QI注册内某些或全部变量的收集。全部或部分 数据收集的自动化可以允许当前致力于手动数据的大量资源 要重新利用的抽象以支持在本地QI活动中更连续，积极主动的参与。到 解决与情节绩效评估相关的限制，分析的替代方法 更实时的数据可用于提供性能下降的预警。退伍军人 事务（VA）外科质量改进计划（VASQIP）是最成功，最长的 常规国家临床登记处用于手术Qi，并且已成为许多类似的模板 私营部门的计划。因此，VASQIP代表了评估替代方案的绝佳模型 数据收集和分析的方法，可以使数据流通过质量更有效 改进周期并增强国家手术质量努力。该提议的总体目标是评估 替代方案，可能更有效的策略，可以在现有的 当代手术QI计划的基础设施并有助于更有效的数据流。具体 目的是：（1）开发和验证MDSM使用结构化和非结构化电子健康记录数据 自动化心脏VASQIP数据收集； （2）比较风险调整后的CUSUM（统计过程 从行业借入的控制方法到季度观察到的比率（即：Vasqip的当前 评估绩效的方法）评估VA医院心脏手术表现； （3）进行半 与潜水员利益相关者群体进行结构化访谈，以设定国家研究议程以进行扩展和 改进手术QI计划。该混合方法提案将涉及使用的观察性研究 VASQIP和VA公司数据仓库数据用于在VA医院接受心脏手术的患者 在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未来 可以改善VASQIP中可用数据的更改。这个项目很重要且新颖，因为它将 提供可用于为国家外科手术和非手术气通知的现实世界中的数据 弗吉尼亚州和私营部门内的倡议。