Landis-Lewis Zach, Flynn Allen, Janda Allison, Shah Nirav
Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, United States.
School of Information, University of Michigan, Ann Arbor, MI, United States.
JMIR Res Protoc. 2022 May 10;11(5):e34990. doi: 10.2196/34990.
Health care delivery organizations lack evidence-based strategies for using quality measurement data to improve performance. Audit and feedback (A&F), the delivery of clinical performance summaries to providers, demonstrates the potential for large effects on clinical practice but is currently implemented as a blunt one size fits most intervention. Each provider in a care setting typically receives a performance summary of identical metrics in a common format despite the growing recognition that precisionizing interventions hold significant promise in improving their impact. A precision approach to A&F prioritizes the display of information in a single metric that, for each recipient, carries the highest value for performance improvement, such as when the metric's level drops below a peer benchmark or minimum standard for the first time, thereby revealing an actionable performance gap. Furthermore, precision A&F uses an optimal message format (including framing and visual displays) based on what is known about the recipient and the intended gist meaning being communicated to improve message interpretation while reducing the cognitive processing burden. Well-established psychological principles, frameworks, and theories form a feedback intervention knowledge base to achieve precision A&F. From an informatics perspective, precision A&F requires a knowledge-based system that enables mass customization by representing knowledge configurable at the group and individual levels.
This study aims to implement and evaluate a demonstration system for precision A&F in anesthesia care and to assess the effect of precision feedback emails on care quality and outcomes in a national quality improvement consortium.
We propose to achieve our aims by conducting 3 studies: a requirements analysis and preferences elicitation study using human-centered design and conjoint analysis methods, a software service development and implementation study, and a cluster randomized controlled trial of a precision A&F service with a concurrent process evaluation. This study will be conducted with the Multicenter Perioperative Outcomes Group, a national anesthesia quality improvement consortium with >60 member hospitals in >20 US states. This study will extend the Multicenter Perioperative Outcomes Group quality improvement infrastructure by using existing data and performance measurement processes.
The proposal was funded in September 2021 with a 4-year timeline. Data collection for Aim 1 began in March 2022. We plan for a 24-month trial timeline, with the intervention period of the trial beginning in March 2024.
The proposed aims will collectively demonstrate a precision feedback service developed using an open-source technical infrastructure for computable knowledge management. By implementing and evaluating a demonstration system for precision feedback, we create the potential to observe the conditions under which feedback interventions are effective.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/34990.
医疗服务提供机构缺乏基于证据的策略来利用质量测量数据改善绩效。审核与反馈(A&F),即向提供者提供临床绩效总结,显示出对临床实践有重大影响的潜力,但目前实施的方式较为粗放,一种模式适用于大多数情况。尽管越来越多的人认识到精准化干预在提高其影响方面具有巨大潜力,但在一个护理环境中的每个提供者通常会收到以通用格式呈现的相同指标的绩效总结。A&F的精准方法优先在单个指标中显示信息,对于每个接收者而言,该指标对绩效改进具有最高价值,例如当该指标水平首次降至同行基准或最低标准以下时,从而揭示一个可采取行动的绩效差距。此外,精准A&F根据对接收者的了解以及要传达的预期主旨含义使用最佳信息格式(包括框架和视觉展示),以改善信息解读,同时减轻认知处理负担。完善的心理学原理、框架和理论构成了反馈干预知识库,以实现精准A&F。从信息学角度来看,精准A&F需要一个基于知识的系统,该系统通过表示在组和个体层面可配置的知识来实现大规模定制。
本研究旨在实施和评估麻醉护理中精准A&F的示范系统,并在一个全国性质量改进联盟中评估精准反馈电子邮件对护理质量和结果的影响。
我们提议通过进行三项研究来实现我们的目标:一项使用以人为本的设计和联合分析方法的需求分析和偏好获取研究、一项软件服务开发和实施研究,以及一项对精准A&F服务进行的集群随机对照试验并同时进行过程评估。本研究将与多中心围手术期结果组合作开展,该组织是一个全国性麻醉质量改进联盟,在美国20多个州拥有60多家成员医院。本研究将利用现有数据和绩效测量流程扩展多中心围手术期结果组的质量改进基础设施。
该提议于2021年9月获得资助,为期4年。目标1的数据收集于2022年3月开始。我们计划进行为期24个月的试验,试验的干预期于2024年3月开始。
拟议的目标将共同展示一个使用开源技术基础设施进行可计算知识管理而开发的精准反馈服务。通过实施和评估精准反馈示范系统,我们有潜力观察反馈干预有效的条件。
国际注册报告识别号(IRRID):PRR1-10.2196/34990。