Usability Testing of a Web Tool for Dissemination and Implementation Science Models.

Dissemination and Implementation science is dedicated to increasing the speed of evidence-based research translated into practice as guided by one or multiple D&I theories, models, and frameworks. The Dissemination and Implementation Models in Health Research and Practice web tool guides users on how to plan, select, combine, adapt, use, and assess theories, models, and frameworks. This paper describes usability testing to update the web tool. Iterative user testing was conducted with implementation science research and clinical participants to facilitate updates and optimize the functionality of the tool. A multi-step protocol involved quantitative and qualitative data collection including a survey, interviews, and a usability testing session. Data from the pre-testing surveys were summarized as frequencies. Data from the usability testing sessions were analyzed using a hybrid adapted deductive rapid matrix qualitative analysis. Data from the interviews were analyzed by deductive a priori coding. Fifteen interviewees represented different research and clinical groups and levels of expertise utilizing D&I TMFs. Participants were purposively selected to represent a range of disciplines and D&I expertise, all invited via one-time email. The 847 total interview comments were reduced by similarity to 259 comments, and 142 were feasible changes fitting the priorities of the web tool. Changes to content, format, and functionality are described in this paper. The iterative usability testing elicited improvements to the web tool including adding more examples, definitions, visuals, and tutorials and simplifying the written content. The web tool remains flexible for additions concerning health equity, de-implementation, and other issues. Supplementary Information: The online version contains supplementary material available at 10.1007/s43477-024-00125-7.

Lung cancer screening in rural primary care practices in Colorado: time for a more team-based approach?

BACKGROUND: Despite lung cancer being a leading cause of death in the United States and lung cancer screening (LCS) being a recommended service, many patients eligible for screening do not receive it. Research is needed to understand the challenges with implementing LCS in different settings. This study investigated multiple practice members and patient perspectives impacting rural primary care practices related to LCS uptake by eligible patients. METHODS: This qualitative study involved primary care practice members in multiple roles (clinicians n = 9, clinical staff n = 12 and administrators n = 5) and their patients (n = 19) from 9 practices including federally qualified and rural health centers (n = 3), health system owned (n = 4) and private practices (n = 2). Interviews were conducted regarding the importance of and ability to complete the steps that may result in a patient receiving LCS. Data were analyzed using a thematic analysis with immersion crystallization then organized using the RE-AIM implementation science framework to illuminate and organize implementation issues. RESULTS: Although all groups endorsed the importance of LCS, all also struggled with implementation challenges. Since assessing smoking history is part of the process to identify eligibility for LCS, we asked about these processes. We found that smoking assessment and assistance (including referral to services) were routine in the practices, but other steps in the LCS portion of determining eligibility and offering LCS were not. Lack of knowledge about screening and coverage, patient stigma, and resistance and practical considerations such as distance to LCS testing facilities complicated completion of LCS compared to screening for other types of cancer. CONCLUSIONS: Limited uptake of LCS results from a range of multiple interacting factors that cumulatively affect consistency and quality of implementation at the practice level. Future research should consider team-based approaches to conduct of LCS eligibility and shared decision making.

Models for Delivering Weight Management in Primary Care: Qualitative Results from the MOST Obesity Study.

BACKGROUND: Obesity is a leading cause of morbidity and mortality in the United States (US). Primary care medical practices can educate patients about the health effects of obesity and help patients with obesity lose and manage their weight. However, implementation of weight management in primary care is challenging. We sought to examine how practices that implement weight management services do so feasibly. METHODS: Multiple methods including site visits, observations, interviews, and document reviews were utilized to identify and learn from primary care practices located across the US. A qualitative multidimensional classification of empirical cases was performed to identify unique delivery features that were feasible to implement in primary care. RESULTS: Across 21 practices, 4 delivery models were identified: group, integrated into standard primary care, hiring an "other" professional, and using a specific program. Model characteristics included who delivered the weight management services, whether delivered to an individual or group, the types of approaches used, and how the care was reimbursed or paid. Most practices integrated weight management services and primary care delivery, although some created specific carve-out programs. CONCLUSION: This study identified 4 models that may serve to overcome challenges in delivering weight management services in primary care. Based on practice characteristics, preferences, and resources, primary care practices can identify a model for successfully implementing weight management services that best fits their context and needs. It is time for primary care to truly address obesity care as the health issue it is and make it a standard of care for all patients with obesity.

Values and Meaning in Rural Primary Care Practices: Implications for Interventions Within Context.

OBJECTIVES: It is important to understand the unique perspectives and values that motivate patients and clinicians in rural primary care settings to participate in clinical care activities. Our objective was to explore perspectives, preferences, and values related to primary care that could influence implementation of evidence-based programs. METHODS: Qualitative study utilizing semi-structured interviews and using immersion/crystallization and thematic analysis. Participants were primary care practice members (clinicians, clinical staff, and administrators) and their patients in rural Colorado. RESULTS: Twenty-six practice members and 23 patients across 9 practices participated. There were 4 emergent themes that were consistent across practice members and some patients. Patient perspectives are located in parenthesis. They included: (1) Focus on quality patient care, patient satisfaction, and continuity of care (patients appreciated quality and compassionate care), (2) Importance of prevention and wellness (patients appreciated help with preventing health problems), (3) Clinician willingness and ability to meet patient preferences for care (patients described comfort with local care), and (4) Passion for serving underserved, uninsured, or vulnerable populations (patients described their vulnerabilities). There were differences in how the perspectives were operationalized by practice member role, illustrating the importance of different ways of addressing these values. CONCLUSIONS: Successful implementation requires consideration of context, and much of context is understanding what is important to those involved in the primary care experience. This study sheds light on salient values of rural primary care practice members and their patients, which may inform interventions designed with and for this setting.

Use of the reach, effectiveness, adoption, implementation, and maintenance (RE-AIM) framework to guide iterative adaptations: Applications, lessons learned, and future directions.

Introduction: Implementation science frameworks have been used widely for planning and evaluation, but seldom to guide adaptations during program implementation. There is great potential for these frameworks to be used to inform conceptual and data-driven decisions about adaptations. Methods: We summarize recent applications using Iterative RE-AIM to capture and guide adaptations. Iterative RE-AIM can be repeated at multiple time points customized to each project and involves the following activities: identification of key implementation partners; rating importance of and progress on each RE-AIM dimension (reach, effectiveness, adoption, implementation, and maintenance); use of summary data on ratings to identify one or two RE-AIM dimensions for adaptations and implementation strategies; and evaluation of progress and impact of adaptations. We summarize recent and ongoing Iterative RE-AIM applications across multiple care coordination and pain management projects within the Veterans Health Administration, a hypertension control trial in Guatemala, a hospital-based lung ultrasound implementation pilot, and a colorectal cancer screening program in underserved communities. Results: Iterative RE-AIM appears feasible, helpful, and broadly applicable across diverse health care issues, interventions, contexts, and populations. In general, the RE-AIM dimension showing the largest gap between importance and progress has been Reach. The dimensions most frequently selected for improvement have been Reach and Implementation. We discuss commonalities, differences and lessons learned across these various applications of Iterative RE-AIM. Challenges include having objective real time data on which to make decisions, having key implementation staff available for all assessments, and rapidly scoring and providing actionable feedback. We discuss print and online resources and materials to support Iterative RE-AIM. Conclusions: The use of Iterative RE-AIM to guide and support understanding of adaptations has proven feasible across diverse projects and in multiple case studies, but there are still questions about its strengths, limitations, essential components, efficiency, comparative effectiveness, and delivery details. Future directions include investigating the optimal frequency and timing for iterative applications; adding contextual assessments; developing more continuous and rapid data on which to make adaptation decisions; identifying opportunities to enhance health equity; and determining the level of facilitation that is most cost-effective.