Wonderings to research questions: Engaging patients in long COVID research prioritization within a learning health system

思考与研究问题:在学习型医疗系统中,如何让患者参与新冠长期症状研究的优先排序

阅读:1

Abstract

BACKGROUND: An integral component of research within a learning health system is patient engagement at all stages of the research process. While there are well-defined best practices for engaging with patients on predetermined research questions, there is little specific methodology for engaging patients at the stage of research question formation and prioritization. Further, with an emerging disease such as Long COVID, population-specific strategies for meaningful engagement have not been characterized. METHODS: The COVID-19 Focused Virtual Patient Engagement Studio (CoVIP studio) was a virtual panel created to facilitate patient-centered studies surrounding the effects of long-term COVID ("Long COVID") also known as post-acute SARS-CoV-2 syndrome (PASC). A diverse group of panelists was recruited and trained in several different areas of knowledge, competencies, and abilities regarding research and Long COVID. A three-step approach was developed that consisted of recording panelists' broad wonderings to generate patient-specific research questions. RESULTS: The "wonderings" discussed in panelists' training sessions were analyzed to identify specific populations, interventions, comparators, outcomes, and timeframes (PICOT) elements, which were then used to create a survey to identify the elements of greatest importance to the panel. Based on the findings, 10 research questions were formulated using the PICOT format. The panelists then ranked the questions on perceived order of importance and distributed one million fictional grant dollars between the five chosen questions in the second survey. Through this stepwise prioritization process, the project team successfully translated panelists' research wonderings into investigable research questions. CONCLUSION: This methodology has implications for the advancement of patient-engaged prioritization both within the scope of Long COVID research and in research on other rare or emerging diseases.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。