Implementation science communications最新文献

Background: Guided by the Interactive Systems Framework and the Evidence-Based System for Implementation Support, the current study examines how County Offices of Education can advance systemic SEL in districts and schools to bolster the well-being and mental health of educators and students.

Method: Through the directed qualitative content analysis of plans drafted by County Leaders, we examine how counties envisioned providing implementation support and provide definitions, examples, and frequencies of five commonly used implementation support strategies in real-world practice.

Results: Tools, trainings, and technical assistance were mentioned in the majority of plans, incentives in approximately half the plans, and feedback loops were referenced in only 27% of the plans. We found that implementation support strategies were innovatively combined to build the capacity of local practitioners in integrated and responsive ways. We also present two new dimensions to conceptualize implementation support and advance theory-building in the field. The first dimension is implementation support strategy orientation, or the degree to which strategies are responsive to local preferences, resulting in a spectrum of top-down to bottom-up approaches. Second, implementation support strategy facilitation, describes the degree to which support strategies range in their level of requisite preparation.

Conclusions: Ultimately, our findings aim to inform implementation planning, advance and refine current frameworks, and narrow the bidirectional gap between research and practice.

Background: As dissemination and implementation research advances, research teams seek guidance on how to apply existing ethical standards to implementation research, given its unique study designs, multi-level intervention targets, and associated risks. This scoping review examines ethical principles and concerns in implementation research, identifies potential risks and unintended consequences, and explores ethical frameworks guiding responsible conduct.

Methods: We searched PubMed, Web of Science, and Google Scholar for relevant literature published between January 2010 and January 2024. The search utilized a combination of standardized terms and keywords, including bioethics, ethics, research standards, responsible research, health services research, and dissemination and implementation science. Two reviewers independently extracted data on study characteristics, ethical considerations, and recommendations. We applied thematic analysis using deductive coding to identify key ethical domains and unintended consequences.

Results: The search returned 1,003 articles, of which 791 unique citations underwent screening. We included 44 articles that discussed ethical principles, risks, unintended consequences, or ethical frameworks in implementation research. The most identified ethical challenges were complexities in informed consent for system or multilevel interventions (55% of articles), equipoise regarding established interventions in new contexts (36%), inconsistencies in regulatory oversight (34%), and the need for robust risk-benefit assessments (32%). Frequently discussed unintended consequences included burnout of health providers (25%), failure to sustain interventions (25%), reduced participant autonomy (23%), stigma (20%), and equity concerns (20%). Three key findings emerged. First, while general ethical principles apply, researchers and review boards struggle with their implementation, particularly in informed consent, equipoise, and risk-benefit assessment. Second, implementation research primarily presents socio-behavioral risks, such as stigma, loss of trust, equity concerns, and burnout, rather than clinical adverse events. Third, existing ethical frameworks remain fragmented, with no consensus standards for oversight.

Conclusions: Ethical challenges in implementation research require clearer oversight and better-adapted frameworks. Existing guidance remains fragmented, leading to inconsistent risk assessments. Addressing socio-behavioral risks and unintended consequences is crucial. Strengthening ethical oversight will require consensus-driven standards, training, and further empirical research on the unique risks posed by implementation research.

Trial registration: The protocol for this manuscript was registered prospectively and published on the Open Science Framework (https://doi.org/10.17605/OSF.IO/7XFWY).

Background: Implementation strategies often fail to achieve their intended outcomes, yet limited methodological guidance exists for systematically understanding why strategies fail or how to adapt them for new contexts. Causal pathway diagrams (CPDs) are tools that map the mechanisms through which implementation strategies work. This study proposes a methodologic approach to use CPDs both retrospectively to understand implementation failure in one context and prospectively to inform adaptation for a new context.

Methods: We illustrate the proposed methodology using a case study of a failed implementation strategy bundle-including an electronic best practice alert and clinical decision support, provider education, and clinical champion-designed to improve metabolic-associated steatotic liver disease (MASLD) screening in a weight management clinic. Using mixed methods data and realist evaluation principles, we constructed CPDs guided by three theoretical frameworks (i-PARIHS, Theory of Planned Behavior, and Technology Acceptance Model) and post-intervention qualitative interviews to understand failure mechanisms. We then applied insights from these CPDs alongside qualitative interviews with primary care providers to develop predictive CPDs for implementing adapted strategies in the primary care setting.

Results: The retrospective CPDs revealed specific failure points not apparent during initial planning. The clinical decision support strategy failed because fundamental preconditions were unmet: only 54.2% of patients had recent laboratory results needed for automated calculations, and the alert placement misaligned with provider workflows. Provider education and clinical champion strategies were undermined by moderators including lack of confidence in the FIB-4 screening tool and competing clinical priorities. The prospective primary care CPDs identified necessary adaptations including replacing best practice alerts with integrated health maintenance reminders, adding content about MASLD complications and treatments to provider education, and selecting multiple champions to ensure adequate coverage.

Conclusions: CPDs provide a systematic framework for transforming implementation failures into actionable insights for future intervention design. The integration of realist evaluation principles with multi-theoretical CPD development offers a replicable methodology for retrospective mechanistic failure analysis and context-adapted prospective implementation. This approach advances implementation science by moving beyond descriptive accounts toward rigorous understanding of how and why implementation strategies work across diverse settings.

Background: The Healthy, Hunger-Free Kids Act (2010) improved the nutritional quality of school meals in the U.S. by aligning the National School Lunch and Breakfast Programs with updated dietary guidelines. However, 2018 federal flexibilities in sodium, whole grains, and milk standards shifted key implementation decisions to local school districts. This created variability in uptake and potential inequities in diet quality and health outcomes, particularly among students from low-resource settings. Key drivers of differential uptake, and decision-making by local districts, is not well understood.

Methods: In this three-phase study, we will use a mixed-method approach to examine the implementation of sodium, whole grains, and milk flexibilities across the many levels involved in implementing the National School Lunch and School Breakfast Programs and describe the impact of flexibility implementation on child health-related outcomes. In phase 1, we will conduct a nationally representative survey of school district food service directors to assess current flexibility implementation practices and determinants of decision-making. We will combine these primary data with publicly available state level and school district data to understand how decisions relate to school meal participation and health-related outcomes. Phase 2 involves interviewing school food industry actors to understand how National School Lunch and School Breakfast Programs policy changes influence decisions that change food supply and distribution. In phase 3, we leverage insights from the actors surveyed and interviewed in phases 1 and 2 to develop an agent-based model. We will use this model to analyze potential effects of different policy changes on child health, examination of ways to improve effective and equitable district-level implementation of the National School Lunch and School Breakfast Program, and to develop tools to inform real-world policymaking.

Discussion: This study offers critical insights into the complexity of school food systems, emphasizing the role of decision making by actors involved in National School Lunch and School Breakfast Program implementation. Results will inform evidence-based strategies to support equitable implementation of school nutrition policies, expand policy implementation science by providing insight into the mechanisms that shape variation in implementation and health-related outcomes, and contribute methodological innovation using an agent-based model as a virtual policy laboratory.

Background: Measuring intervention fidelity can help justify the transition of evidence-based practices into everyday community-based practices by helping identify components of the intervention supporting the intervention's uptake. Current methods to measure fidelity include self-reported data, observational data using audio or video recordings, and in-vivo observations, but these are limited due to cost-ineffectiveness, time inefficiency, and external validity threats. Photo coding is proposed as a new, pragmatic method of observational data to measure intervention fidelity, as was used in a pharmacy system re-design intervention (Senior Safe™) to improve over-the-counter (OTC) medication safety for older adults.

Methods: Guided by human factors engineering principles, Senior Safe re-shelved OTC medications based on safety level, and signage was added to designate safer and high-risk products. Senior Safe was implemented by pharmacy leadership and maintained by pharmacy staff. Pharmacy leadership and researchers collaborated to take photos of Senior Safe two times (Time 1 and Time 2), at least three months apart, to examine intervention layout and medication categorization fidelity. A Layout Codebook was constructed to evaluate the conformity of signage to ergonomic principles. A Medication Categorization Codebook was designed to assess whether the signage was properly allocated to the designated products. Two research assistants and two PharmD students coded the photos. A fidelity standard of ≥ 80% concordance with intervention guidelines was used to signify high-fidelity.

Results: Fidelity was assessed within 67 pharmacy sites implementing Senior Safe. All sites achieved ≥ 80% fidelity concordance between Time 1 and Time 2 for the Layout Codebook. Alternatively, for the Medication Categorization Codebook, 97% of sites met high-fidelity standards during Time 1, compared to 85% of sites at Time 2, indicating statistically significant negative drift. Overall, over half of all Time 1 and Time 2 concordance rates involving the Layout and Medication Categorization Codebooks exceeded the ≥ 80% high-fidelity standard.

Conclusion: Measuring intervention fidelity is useful in determining whether interventions are sustained and successfully transitioned into community-based practice. Photo coding is an innovative approach to measure intervention fidelity and allows researchers to identify multiple layers of concordance and discordance to intervention guidelines. Organizations are encouraged to investigate intervention fidelity, including identifying discordance and adaptation needs.

Background: Machine learning (ML)-a field of study dedicated to the principled extraction of knowledge from complex data-can benefit implementation science, quality improvement (QI), and primary care research. Given the general complexity of implementation research and the need to develop strategies for understanding relationships among practice characteristics and practice facilitation strategies, we chose the Implementation Research Logic Model (IRLM) as an underlying structure for the data and to identify relationships that might be associated with outcomes. This study illustrates this novel method involving ML and an IRLM in the context of a practice facilitation-supported QI program in primary care.

Methods: We applied advanced statistical methods within a machine learning framework to data from the Healthy Hearts in the Heartland (H3) study, including practice facilitation data and practice and staff participation survey, to assess the relationship between practice attributes and practice facilitator strategies and their impact on successful implementation of QI interventions. We used PCA for feature selection, incorporated practice facilitators' knowledge for contextual factor validation, and employed Structural Equation Modeling (SEM) to analyze relationships among contextual factors, latent variables, practice facilitation strategies, and outcomes.

Results: We selected 20 contextual factors and identified practice facilitation strategies and mapped them to the IRLM. Cronbach's alphas of contextual factors in the five domains (Intervention characteristics, outer setting, inner setting, characteristics of individuals, and implementation process) are 0.71, 0.82, 0.72, 0.89, 0.86, respectively. We used structural equation modeling to analyze the relationships among contextual factors, latent variables, practice facilitation strategies (Doing Tasks, Project Management, Consulting, Teaching, and Coaching), and outcomes (number of implemented QI interventions and Change Process Capability Questionnaire (CPCQ) score). All five facilitation strategies had statistically significant associations with the implementation of QI interventions (all P < 0.05).

Conclusions: The combination of ML and the theory behind the IRLM can be used to identify relationships between inner and outer context determinants and implementation strategies and study outcomes in pragmatic research study datasets. All the proposed strategies in H3 were statistically associated with completed QI interventions; and the strategies had more impact on the implementation of interventions than CPCQ change. By understanding the relationship between outcomes, practice determinants and coaching strategies, practice facilitators can better help primary care practices adapt and implement interventions and build capacity to adapt to change.

Background: Unhealthy alcohol use is a public health problem with significant health, social and economic impacts. Alcohol screening and brief intervention (ASBI) in adult primary care is an evidence-based approach enabling early identification and intervention of unhealthy alcohol use. However, large-scale implementation and sustainment of ASBI in routine clinical practice remains a challenge, and little is known about its population-level impact. Using electronic health record (EHR) data in a large integrated healthcare system in Northern California that implemented systematic ASBI in adult primary care in mid-2013, this observational study examined: 1) trajectories of ASBI performance over 5 years post systematic implementation, and 2) their associations with both later ASBI performance and alcohol use outcomes.

Methods: Using the health plan's EHR data, we calculated annual screening rates of adults with a primary care visit, and brief intervention (BI) rates among those with a positive screen (i.e., reporting alcohol consumption exceeding the age and sex specific daily and weekly low-risk National Institute on Alcohol Abuse and Alcoholism guidelines), for 57 medical facilities from years 2014 to 2021. We conducted latent class growth analysis using annual screening and BI rates to characterize trajectories of ASBI performance from years 2014-2018. Multivariable mixed-effects models were fit to examine the associations of ASBI performance trajectories with later ASBI performance and facility-level alcohol use outcomes.

Results: Three distinct screening performance trajectory groups (low-, middle- and high-performance) and four distinct BI performance trajectory groups (low-, improving-, middle- and high-performance) were identified. Facilities in the low-BI-performance group had panels of patients living in more deprived neighborhoods compared to the other 3 BI performance groups. After accounting for repeated measures and adjusting for time and patient panel characteristics, we found that screening and BI performance trajectories during 2014-2018 were significantly associated with screening and BI rates 2019-2021, respectively. We also observed a steeper decline in percentages reporting "exceeding daily drinking limits" and "having 5 + binge drinking days" over time among patients of facilities in the improving- and high-BI-performance groups.

Conclusions: Early success in ASBI performance is associated with long-term sustainability and may be associated with long-term population-level drinking outcomes.

Background: Implementation science has become increasingly important for improving uptake of healthcare innovations, which typically involves a broad range of stakeholders. The Acceptability of Intervention Measure (AIM), Intervention Appropriateness Measure (IAM), and Feasibility of Intervention Measure (FIM) are generic and adaptable outcome measures to assess the implementation of innovations across various settings and populations. However, their use in Denmark requires translation into the Danish language and a cross-cultural adaptation into the Danish healthcare context.

Methods: The study aimed to translate and cross-culturally adapt the AIM, IAM, and FIM for use in Danish healthcare settings. The translation process followed Beaton's guidelines, encompassing six stages: translation, synthesis, backward translation, expert committee review, pretesting, and appraisal of the adaptation process. Both quantitative (questionnaires) and qualitative (interviews) methods were applied during pretesting to evaluate the Danish versions.

Results: All stages of the translation and adaption process were completed. Linguistic challenges were identified, such as ensuring distinction between items, but they were resolved during the expert review. Pretesting with 33 Danish healthcare professionals showed that items were generally clear and relevant, but some overlap between AIM, IAM, and FIM items caused confusion. For example, IAM item 4 ("… seems like a good match") was difficult to interpret, leading to missing responses, and FIM item 3 was revised to improve clarity.

Conclusions: The translation and cross‑cultural adaptation, including pretesting, of the AIM, IAM, and FIM resulted in Danish versions that maintained conceptual alignment with the originals. While additional evaluation across interventions, contexts, and practices will strengthen the evidence base, the current versions already provide a practical tool for assessing implementation outcomes in Danish healthcare contexts.

Background: Electronic health record (EHR) systems have been used to support the implementation of evidence-based care. Growing evidence suggests that EHR systems can also support de-implementation of low-value care. However, a review of this literature has not been conducted. This scoping review will: 1) summarize how EHR-based interventions themselves have been used in primary care settings to de-implement low-value care, 2) summarize the effectiveness of these EHR interventions, 3) describe de-implementation strategies and outcome measures that have been used, and 4) describe facilitators and barriers that influence EHR-based de-implementation interventions.

Methods: We conducted a search using MEDLINE, CINAHL, Embase, and Web of Science on January 19, 2024 for peer-reviewed papers on EHRs and de-implementation in primary care. We inductively developed themes of how the EHR was used to support de-implementation. We mapped de-implementation strategies to a previously published taxonomy on implementation strategies, de-implementation outcomes to a previously published taxonomy on these outcomes, and facilitators and barriers to the Consolidated Framework for Implementation Research. We stratified study findings by EHR intervention type.

Results: We included 50 studies. EHRs supported de-implementation using four intervention types: 1) EHR alerts, 2) order sets and preference lists, 3) documentation templates, and 4) communication tools among the care team. The proportion of studies that showed favorable effectiveness in reducing low-value care ranged from 16.7% (communication tools) to 50.0% (documentation templates). Common strategies to support EHR-based de-implementation interventions included auditing and providing feedback, conducting educational meetings, and distributing educational materials. Twenty-two studies reported some assessment of de-implementation outcomes. Most EHR intervention types had numerous multi-level facilitators and barriers identified.

Conclusions: This scoping review identified multiple EHR-based interventions that health systems use to support de-implementation and their effectiveness. Although promising, the evidence base is limited by the general lack of frameworks used for intervention development and de-implementation, unclear theoretical rationale to support the use of selected de-implementation strategies, and the unclear validity of de-implementation outcomes used. Additional research is needed to develop and validate frameworks and outcomes for de-implementation to strengthen the evidence base.

Trial registration: None.