Abstract
Communities are dealing with persistent health problems, despite the enormous investment in health research, service delivery, and program development to address those health concerns. While there may be an evidence base for addressing some community health concerns, too often there is incomplete or no medical evidence for addressing many concerns. The High Plains Research Network and Colorado Research Network have used an appreciative inquiry approach to their work for several years, identifying positive aspects of care and developing programs to replicate what is working. Based on five years of informal appreciative-inquiry research and five formal appreciative inquiry projects, we have developed a standard process and method for conducting appreciative-inquiry guided Boot Camp Translations. The purpose of this methodology manuscript is to describe the general approach of using appreciative inquiry as a research tool and the standard process for conducting appreciative inquiry as a patient engagement tool to identify local evidence and develop local solutions.
Highlights
Relevant and actionable health messages and dissemination strategies (Westfall et al, 2013, 2016)
The assets-focus of the Appreciative Inquiry (AI) method resonated with High Plains Research Network (HPRN) C.A.C. members, who shared their stories of successful interactions or experiences with primary care clinicians and other providers
We found that individual interviews delivered more robust, individually-focused stories of success and chose to use only individual interviews for the remaining three AI/Boot Camp Translation (BCT)
Summary
Relevant and actionable health messages and dissemination strategies (Westfall et al, 2013, 2016). The teams of community members include farmers, ranchers, schoolteachers, students, retirees, laborers, and small business owners These groups have developed working agreements (Westfall et al, 2013), IRB protocols (Westfall et al, 2017), patient and practice initiatives (Allison et al, 2014), presented at national and international research conferences together, and co-authored manuscripts and book chapters (Norman et al, 2013; Westfall et al, 2020; Zittleman et al, 2020). As with most evidence, even AIgenerated data needs translation into practical messages that resonate with community members and can be more widely implemented Applying this new evidence to the BCT process allows patients and community members to rapidly and efficiently translate the AI successes into locally-relevant programs and sustainable care. If all healthcare is local (Klein et al, 2017), this process can help communities develop local healthcare solutions
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