Testing the appreciative inquiry and boot camp translation methods for identifying and sharing local solutions to health care issues / Donald E. Nease [and seven others].

Format:

Book

Author/Creator:

Nease, Donald E., author.

Series:

Final research report (Patient-Centered Outcomes Research Institute (U.S.))

Language:

English

Subjects (All):

Community-based research.

Physical Description:

1 online resource (46 pages).

Place of Publication:

Washington, D.C. : Patient-Centered Outcomes Research Institute (PCORI), 2019.

Summary:

BACKGROUND: Our team sought to identify and pilot test the Appreciative Inquiry/Boot Camp Translation (AI/BCT) method to detect and translate local evidence of success with important health topics and concerns. For local health concerns for which suitable evidence is not available, AI generates evidence by engaging patients who have found solutions. BCT translates evidence into locally meaningful messages, calls to action, and materials. We sought to test the combined AI/BCT methodology as a patient-centered method to generate patient-centered evidence and interventions. OBJECTIVES: Conduct 6 projects with underserved rural and urban Colorado communities using the AI/BCT method to select priority health topics, identify factors that facilitate successful health outcomes related to the topic, and translate local evidence-based recommendations into local solutions.1. Identify and describe the components of the AI/BCT method essential to engaging patients and community members in patient-centered research.2. Produce a training program for patients, health care professionals, and academic researchers to disseminate AI/BCT to improve patient engagement for patient-centered outcomes. METHODS: 1. Objective 1: Community members in rural and urban areas and academic partners used AI/BCT to address the following topics: rural access to mental health support, urban access to mental health support, chronic pain management, patient-centered medical home implementation, and sleep apnea diagnosis and treatment.2. Objective 2: We conducted a formative evaluation to identify the key AI/BCT content and process components.3. Objective 3: We compiled the essential AI/BCT components into an educational curriculum and evaluated both an extended and a brief AI/BCT training. RESULTS: 1. Aim 1: Five AI/BCTs successfully generated community-sourced solutions to local health concerns and translated these into constructs and language accessible to patients, community members, and practices in diverse, underserved urban and rural communities. The AI process required more resources than originally anticipated, resulting in the completion of only 5 AI/BCT projects instead of 6.2. Aim 2: We identified 5 essential components of the AI/BCT method. First, selecting topics suitable for AI is critical. Second, the focus on "what worked" for AI interviews is required but might be a challenge for researchers trained in problem-focused approaches. Third, the qualitative analyst must have a solid understanding of AI and BCT. Fourth, project leaders must ensure adequate resources of time and team members. Fifth, the way AI data are presented for BCT is important: BCT facilitators and medical experts should illustrate the local relevance of the AI data and the rigor used to collect and analyze that data.3. Aim 3: Evaluation of our 1.5-day training showed 100% agreement that AI/BCT could help trainees partner with communities in research; 89% of participants agreed that the training taught them the skills they needed to use AI/BCT successfully in research. CONCLUSIONS: AI/BCT is an effective method to address local health concerns for which evidence is not readily available. We successfully tested this method and produced a program for training others in its use in their communities and patient populations. LIMITATIONS AND SUBPOPULATION CONSIDERATIONS: Not all topics are a good fit for AI/BCT. The method requires stories of successfully addressing the selected health topic.

Contents:

Background

Patient and Stakeholder Participation

Methods

Results

Discussion

Conclusions

References

Related Publication

Acknowledgments.

Notes:

Description based on publisher supplied metadata and other sources.