Project description:BackgroundThe store-and-forward camera-based evaluation of the eye, or teleophthalmology, is an effective way to identify diabetic retinopathy, the leading cause of blindness in the United States, but uptake has been slow. Understanding the barriers to and facilitators of implementing teleophthalmology programs from those actively adopting, running, and sustaining such programs is important for widespread adoption.ObjectiveThis study aims to understand the factors that are important in introducing teleophthalmology to improve access to diagnostic eye care for patients with diabetes in primary care clinics by using implementation science.MethodsThis qualitative study in 3 urban, low-income, largely racial and ethnic minority-serving safety-net primary care clinics in Rochester, New York, interviewed nurses and physicians on implementing a teleophthalmology program by using questions informed by the Practical, Robust Implementation and Sustainability Model and the Consolidated Framework for Implementation Research.ResultsPrimary care nurses operationalizing the program in their clinics saw increased work burden and a lack of self-efficacy as barriers. Continuous training on the teleophthalmology process for nurses, physicians, and administrative staff through in-service and peer training by champions and superusers were identified by interviewees as needs. Facilitators included the perceived convenience for the patient and a perceived educational advantage to the program, as it gave an opportunity for providers to discuss the importance of eye care with patients. Concerns in making and tracking referrals to ophthalmology because of challenges related to care coordination were highlighted. The financial aspects of the program (eg, patient coverage and care provider reimbursement) were unclear to many staff members, influencing adoption and sustainability.ConclusionsStreamlining processes and workflows, training and assigning adequate staff, effectively coordinating care between primary care and eye care to improve follow-ups, and ensuring financial viability can all help streamline the adoption of teleophthalmology.

Project description:BackgroundImplementation science aims to accelerate the public health impact of evidence-based interventions. However, implementation science has had too little focus on the role of health policy - and its inseparable politics, polity structures, and policymakers - in the implementation and sustainment of evidence-based healthcare. Policies can serve as determinants, implementation strategies, the evidence-based "thing" to be implemented, or another variable in the causal pathway to healthcare access, quality, and patient outcomes. Research describing the roles of policy in dissemination and implementation (D&I) efforts is needed to resolve persistent knowledge gaps about policymakers' evidence use, how evidence-based policies are implemented and sustained, and methods to de-implement policies that are ineffective or cause harm. Few D&I theories, models, or frameworks (TMF) explicitly guide researchers in conceptualizing where, how, and when policy should be empirically investigated. We conducted and reflected on the results of a scoping review to identify gaps of existing Exploration, Preparation, Implementation, and Sustainment (EPIS) framework-guided policy D&I studies. We argue that rather than creating new TMF, researchers should optimize existing TMF to examine policy's role in D&I. We describe six recommendations to help researchers optimize existing D&I TMF. Recommendations are applied to EPIS, as one example for advancing TMF for policy D&I.Recommendations(1) Specify dimensions of a policy's function (policy goals, type, contexts, capital exchanged). (2) Specify dimensions of a policy's form (origin, structure, dynamism, outcomes). (3) Identify and define the nonlinear phases of policy D&I across outer and inner contexts. (4) Describe the temporal roles that stakeholders play in policy D&I over time. (5) Consider policy-relevant outer and inner context adaptations. (6) Identify and describe bridging factors necessary for policy D&I success.ConclusionResearchers should use TMF to meaningfully conceptualize policy's role in D&I efforts to accelerate the public health impact of evidence-based policies or practices and de-implement ineffective and harmful policies. Applying these six recommendations to existing D&I TMF advances existing theoretical knowledge, especially EPIS application, rather than introducing new models. Using these recommendations will sensitize researchers to help them investigate the multifaceted roles policy can play within a causal pathway leading to D&I success.

Project description:RationaleThe host-pathogen relationship is inherently dynamic and constantly evolving. Applying an implementation science lens to policy evaluation suggests that policy impacts are variable depending upon key implementation outcomes (feasibility, acceptability, appropriateness costs) and conditions and contexts.Covid-19 case studyExperiences with non-pharmaceutical interventions (NPIs) including masking, testing, and social distancing/business and school closures during the COVID-19 pandemic response highlight the importance of considering public health policy impacts through an implementation science lens of constantly evolving contexts, conditions, evidence, and public perceptions. As implementation outcomes (feasibility, acceptability) changed, the effectiveness of these interventions changed thereby altering public health policy impact. Sustainment of behavioral change may be a key factor determining the duration of effectiveness and ultimate impact of pandemic policy recommendations, particularly for interventions that require ongoing compliance at the level of the individual.Practical framework for assessing and evaluating pandemic policyUpdating public health policy recommendations as more data and alternative interventions become available is the evidence-based policy approach and grounded in principles of implementation science and dynamic sustainability. Achieving the ideal of real-time policy updates requires improvements in public health data collection and analysis infrastructure and a shift in public health messaging to incorporate uncertainty and the necessity of ongoing changes. In this review, the Dynamic Infectious Diseases Public Health Response Framework is presented as a model with a practical tool for iteratively incorporating implementation outcomes into public health policy design with the aim of sustaining benefits and identifying when policies are no longer functioning as intended and need to be adapted or de-implemented.Conclusions and implicationsReal-time decision making requires sensitivity to conditions on the ground and adaptation of interventions at all levels. When asking about the public health effectiveness and impact of non-pharmaceutical interventions, the focus should be on when, how, and for how long they can achieve public health impact. In the future, rather than focusing on models of public health intervention effectiveness that assume static impacts, policy impacts should be considered as dynamic with ongoing re-evaluation as conditions change to meet the ongoing needs of the ultimate end-user of the intervention: the public.

Project description:BackgroundOrganizational readiness is a key factor for successful implementation of evidence-based interventions (EBIs), but a valid and reliable measure to assess readiness across contexts and settings is needed. The R = MC2 heuristic posits that organizational readiness stems from an organization's motivation, capacity to implement a specific innovation, and its general capacity. This paper describes a process used to examine the face and content validity of items in a readiness survey developed to assess organizational readiness (based on R = MC2) among federally qualified health centers (FQHC) implementing colorectal cancer screening (CRCS) EBIs.MethodsWe conducted 20 cognitive interviews with FQHC staff (clinical and non-clinical) in South Carolina and Texas. Participants were provided a subset of items from the readiness survey to review. A semi-structured interview guide was developed to elicit feedback from participants using "think aloud" and probing techniques. Participants were recruited using a purposive sampling approach and interviews were conducted virtually using Zoom and WebEx. Participants were asked 1) about the relevancy of items, 2) how they interpreted the meaning of items or specific terms, 3) to identify items that were difficult to understand, and 4) how items could be improved. Interviews were transcribed verbatim and coded in ATLAS.ti. Findings were used to revise the readiness survey.ResultsKey recommendations included reducing the survey length and removing redundant or difficult to understand items. Additionally, participants recommended using consistent terms throughout (e.g., other units/teams vs. departments) the survey and changing pronouns (e.g., people, we) to be more specific (e.g., leadership, staff). Moreover, participants recommended specifying ambiguous terms (e.g., define what "better" means).ConclusionUse of cognitive interviews allowed for an engaged process to refine an existing measure of readiness. The improved and finalized readiness survey can be used to support and improve implementation of CRCS EBIs in the clinic setting and thus reduce the cancer burden and cancer-related health disparities.

Project description:BackgroundTo meet the growing demand for implementation science expertise, building capacity is a priority. Various training opportunities have emerged to meet this need. To ensure rigor and achievement of specific implementation science competencies, it is critical to systematically evaluate training programs.MethodsThe Penn Implementation Science Institute (PennISI) offers 4 days (20 h) of virtual synchronous training on foundational and advanced topics in implementation science. Through a pre-post design, this study evaluated the sixth PennISI, delivered in 2022. Surveys measures included 43 implementation science training evaluation competencies grouped into four thematic domains (e.g., items related to implementation science study design grouped into the "design, background, and rationale" competency category), course-specific evaluation criteria, and open-ended questions to evaluate change in knowledge and suggestions for improving future institutes. Mean composite scores were created for each of the competency themes. Descriptive statistics and thematic analysis were completed.ResultsOne hundred four (95.41% response rate) and 55 (50.46% response rate) participants completed the pre-survey and post-survey, respectively. Participants included a diverse cohort of individuals primarily affiliated with US-based academic institutions and self-reported as having novice or beginner-level knowledge of implementation science at baseline (81.73%). In the pre-survey, all mean composite scores for implementation science competencies were below one (i.e., beginner-level). Participants reported high value from the PennISI across standard course evaluation criteria (e.g., mean score of 3.77/4.00 for overall quality of course). Scores for all competency domains increased to a score between beginner-level and intermediate-level following training. In both the pre-survey and post-survey, competencies related to "definition, background, and rationale" had the highest mean composite score, whereas competencies related to "design and analysis" received the lowest score. Qualitative themes offered impressions of the PennISI, didactic content, PennISI structure, and suggestions for improvement. Prior experience with or knowledge of implementation science influenced many themes.ConclusionsThis evaluation highlights the strengths of an established implementation science institute, which can serve as a model for brief, virtual training programs. Findings provide insight for improving future program efforts to meet the needs of the heterogenous implementation science community (e.g., different disciplines and levels of implementation science knowledge). This study contributes to ensuring rigorous implementation science capacity building through the evaluation of programs.

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What do you think it means? Using cognitive interviewing to improve measurement in implementation science: description and case example

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