Development of an Adverse Event Surveillance System for Outpatient Surgery

门诊手术不良事件监测系统的开发

基本信息

  • 批准号:
    8780231
  • 负责人:
  • 金额:
    --
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2014
  • 资助国家:
    美国
  • 起止时间:
    2014-10-01 至 2018-09-30
  • 项目状态:
    已结题

项目摘要

DESCRIPTION (provided by applicant): This is the second submission of an HSR&D CDA-2 proposal to provide four years of salary support to Hillary Mull, Ph.D. toward her goal of developing an independent VA health services research career. Dr. Mull is an Investigator at the Center for Healthcare Organization and Implementation Research (CHOIR), a joint HSR&D Center of Innovation (COIN) of two former Centers of Excellence, the Center for Organization, Leadership, and Management Research (COLMR) at the VA Boston Healthcare System and the Center for Healthcare Quality Outcomes, and Economics Research (CHQOER) at the Bedford VAMC. Dr. Mull is also a Research Assistant Professor at the Boston University School of Medicine (BUSM) in the Department of Surgery. This CDA proposal outlines research and training plans that will set the foundation for Dr. Mull's future as an independent investigator with significant experience in applying healthcare informatics tools to detect outpatient adverse events (AEs). Her CDA goal is to improve quality and safety in outpatient surgery through the development and implementation of an AE surveillance system that uses trigger tools (TTs) to screen outpatient surgical data in the VA Corporate Data Warehouse (CDW). TTs are an emerging method that can increase the efficiency of the chart review process. TTs screen electronic clinical data to flag cases with a high probability of having an AE; cases are then briefly reviewed by a nurse to confirm the presence of any AE, and to rate the severity of the harm. In Aim 1, Dr. Mull will create a database of outpatient surgeries in VA through a mixed methods approach that uses qualitative interviews with surgical stakeholders to identify relevant variables associated with select TT outcomes (e.g., visit to the emergency department following outpatient surgery). These variables will be drawn from a modified Donabedian conceptual framework of structural, procedural and patient characteristics. Outpatient surgeries and surgical characteristics will be identified in VA electronic data sources including the CDW. In Aim 2, Dr. Mull will develop a TT surveillance system for outpatient surgery that uses predictive modeling to detect AEs among trigger-flagged cases. Aim 3 will implement the TT system in two VA surgical programs. Dr. Mull will use methods outlined in the Consolidated Framework for Implementation Research (CFIR) to conduct a formative evaluation of the implementation process, and summative evaluation of the effect of the TT system on AE rates and outpatient surgical quality. Dr. Mull's training plan focuses on bolstering her knowledge of healthcare informatics, qualitative program evaluation, and implementation science. Dr. Mull has a team of expert mentors (including Drs. Amy Rosen, Marty Charns, Kamal Itani, and Mary Hawn) and consultants (Drs. Peter Rivard, Steven Pizer, and Kathleen Hickson) who are qualified and strongly committed to helping her complete her CDA goals and establish herself as an independent HSR&D investigator. This mentorship, along with the rich resources available at CHOIR, will ensure that Dr. Mull has all the support necessary to meet her research and training aims. She will have access to CHOIR facilities, resources, and collaborations with exceptional HSR&D-funded investigators. Through this CDA, and in a QUERI SDP proposal planned for year 2 and an HSR&D pilot planned for year 4 of the CDA work, Dr. Mull proposes to develop and implement an AE surveillance system designed for outpatient surgery that can be used to target quality improvement efforts. The system can be broadly applicable to other outpatient settings, something we will explore in future research. Detecting and monitoring AEs in outpatient settings using existing data in the VA CDW is consistent with HSR&D funding priority C- Healthcare Informatics. This field of research will become even more important as care increasingly transitions from the hospital to the outpatient setting. Our results will provide much needed information about VA outpatient surgery to the research and surgical communities as they continue to measure and improve the quality of surgical care to our Veterans.
描述(由申请人提供): 这是第二次提交HSR&D CDA-2提案,向Hillary Mull博士提供四年的工资支持,以实现她发展独立的退伍军人健康服务研究事业的目标。穆尔博士是医疗保健组织和实施研究中心(CHIIR)的研究员,该中心是由两个前卓越中心--退伍军人管理局波士顿医疗系统的组织、领导力和管理研究中心(COLMR)和贝德福德VAMC的经济研究中心(CHQOER)组成的联合HSR&D创新中心(COIN)。穆尔博士也是波士顿大学医学院(BUSM)外科系的研究助理教授。这份CDA提案概述了研究和培训计划,这些计划将为Mull博士未来成为一名独立调查员奠定基础,他在应用医疗保健信息学工具检测门诊不良事件(AE)方面拥有丰富的经验。她的CDA目标是通过开发和实施AE监视系统来提高门诊手术的质量和安全性,该系统使用触发工具(TTS)来筛选VA公司数据仓库(CDW)中的门诊手术数据。TTS是一种新兴的方法,可以提高图表审查过程的效率。TTS筛选电子临床数据,以标记出患有AE的高概率病例;然后,护士对病例进行简要审查,以确认是否存在AE,并对损害的严重性进行评级。在目标1中,穆尔博士将通过混合方法创建退伍军人事务部门诊手术数据库,该方法使用与外科利益相关者的定性访谈来确定与选定的TT结果相关的相关变量(例如,门诊手术后到急诊科就诊)。这些变量将从修改的唐纳贝德概念框架中提取,包括结构、程序和患者特征。门诊手术和手术特征将在包括CDW在内的退伍军人管理局电子数据来源中确定。在目标2中,穆尔博士将为门诊手术开发一个TT监测系统,该系统使用预测性建模来检测触发标记病例中的不良反应。AIM 3将在两个退伍军人管理局手术项目中实施TT系统。穆尔博士将使用实施研究综合框架(CFIR)中概述的方法对实施过程进行形成性评估,并对TT系统对AE发生率和门诊手术质量的影响进行总结性评估。穆尔博士的培训计划侧重于加强她在医疗保健信息学、定性计划评估和实施科学方面的知识。穆尔博士有一支专家导师团队(包括艾米·罗森博士、马蒂·查恩斯博士、卡迈勒·伊塔尼博士和玛丽·霍恩博士)和顾问(彼得·里瓦德博士、史蒂文·皮泽博士和凯瑟琳·希克森博士),他们都是合格的,并坚定地致力于帮助她完成CDA目标,并将自己确立为一名独立的HSR&D调查员。这种指导,加上合唱团丰富的资源,将确保穆尔博士获得所有必要的支持,以实现她的研究和培训目标。她将获得合唱团的设施、资源,并与特别的HSR&D资助的调查人员合作。通过这项CDA,以及在计划于第二年进行的QUERI SDP提案和计划在CDA工作第四年进行的HSR&D试点项目中,Mull博士建议开发和实施一个专为门诊手术设计的AE监测系统,该系统可用于针对质量改进工作。该系统可以广泛适用于其他门诊设置,这是我们将在未来的研究中探索的。使用VA CDW中的现有数据检测和监控门诊环境中的AEs符合HSR&D资金优先C-Healthcare Informatics。随着护理越来越多地从医院过渡到门诊,这一研究领域将变得更加重要。我们的结果将为研究和外科社区提供急需的退伍军人门诊手术信息,因为他们正在继续衡量和改进退伍军人的手术护理质量。

项目成果

期刊论文数量(7)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Defining Outpatient Surgery: Perspectives of Surgical Staff in the Veterans Health Administration.
定义门诊手术:退伍军人健康管理局外科人员的观点。
  • DOI:
  • 发表时间:
    2016
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Mull,HillaryJ;Rosen,AmyK;Rivard,PeterE;Itani,KamalMF
  • 通讯作者:
    Itani,KamalMF
Development of an Adverse Event Surveillance Model for Outpatient Surgery in the Veterans Health Administration.
退伍军人健康管理局门诊手术不良事件监测模型的开发。
  • DOI:
    10.1111/1475-6773.13037
  • 发表时间:
    2018
  • 期刊:
  • 影响因子:
    3.4
  • 作者:
    Mull,HillaryJ;Itani,KamalMF;Pizer,StevenD;Charns,MartinP;Rivard,PeterE;McIntosh,Nathalie;Hawn,MaryT;Rosen,AmyK
  • 通讯作者:
    Rosen,AmyK
The Nature and Severity of Adverse Events in Select Outpatient Surgical Procedures in the Veterans Health Administration.
退伍军人健康管理局部分门诊手术中不良事件的性质和严重程度。
  • DOI:
    10.1097/qmh.0000000000000177
  • 发表时间:
    2018
  • 期刊:
  • 影响因子:
    1.2
  • 作者:
    Mull,HillaryJ;Itani,KamalMF;Charns,MartinP;Pizer,StevenD;Rivard,PeterE;Hawn,MaryT;Rosen,AmyK
  • 通讯作者:
    Rosen,AmyK
Emergency Department Use After Outpatient Surgery Among Dually Enrolled VA and Medicare Patients.
  • DOI:
    10.1097/qmh.0000000000000225
  • 发表时间:
    2019
  • 期刊:
  • 影响因子:
    1.2
  • 作者:
    Mull HJ;Rosen AK;Charns MP;OʼBrien WJ;Hawn MT;Itani KMF;Pizer SD
  • 通讯作者:
    Pizer SD
Factors Associated with Hospital Admission after Outpatient Surgery in the Veterans Health Administration.
与退伍军人健康管理局门诊手术后入院相关的因素。
  • DOI:
    10.1111/1475-6773.12826
  • 发表时间:
    2018
  • 期刊:
  • 影响因子:
    3.4
  • 作者:
    Mull,HillaryJ;Rosen,AmyK;O'Brien,WilliamJ;McIntosh,Nathalie;Legler,Aaron;Hawn,MaryT;Itani,KamalMF;Pizer,StevenD
  • 通讯作者:
    Pizer,StevenD
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Hillary Jane Mull其他文献

Hillary Jane Mull的其他文献

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{{ truncateString('Hillary Jane Mull', 18)}}的其他基金

How Can We Make Invasive Non-Surgical Procedures Safer? Using Big Data to Identify Adverse Events and Opportunities to Mitigate Harm
我们如何才能使侵入性非手术程序更安全?
  • 批准号:
    10159112
  • 财政年份:
    2020
  • 资助金额:
    --
  • 项目类别:
How Can We Make Invasive Non-Surgical Procedures Safer? Using Big Data to Identify Adverse Events and Opportunities to Mitigate Harm
我们如何才能使侵入性非手术程序更安全?
  • 批准号:
    10399528
  • 财政年份:
    2020
  • 资助金额:
    --
  • 项目类别:
Cost-Effectiveness of Dabigatran and Warfarin for Veterans with Afib
达比加群和华法林对患有心房颤动的退伍军人的成本效益
  • 批准号:
    10027257
  • 财政年份:
    2016
  • 资助金额:
    --
  • 项目类别:

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