This chapter explores the role of implementation science for improving the adoption, implementation, scale-up, and long-term use of evidence-based health interventions. It describes the research-to-practice gap and provides a brief history of efforts to address this gap that has culminated in the advancement of implementation science as a recognized field in health and healthcare. The nature of evidence and the factors affecting decisions to adopt will be discussed along with the theories, models, and strategies that guide implementation science with a specific focus on scale-up and sustainability. Key examples of scale-up from the global perspective will be provided along with lessons learned regarding sustainability and deimplementation as well as key next steps for moving the field forward will be provided.

In Antimicrobial Stewardship and Infection Prevention and Control, programmatic goals often strive to achieve clinical benefit by practice change in the direction of doing less. Practically, this may include reducing the number of tests ordered, encouraging shorter and more narrow courses of antimicrobials, or discontinuing practices that are no longer contextually appropriate. Because promoting practice change in the direction of doing less is a critical aspect of day-to-day operations in Antimicrobial Stewardship and Infection Prevention and Control, the goals of this Society for Healthcare Epidemiology Research Committee White Paper are to provide a roadmap and framework for leveraging principles of implementation and de-implementation science in day-to-day practice. Part II of this series focuses on some practical case studies, including real-world examples of applied de-implementation science to promote discontinuation of practices that are ineffective, overused, or no longer effective.

Technical assistance (TA) has long been a strategy utilized to support implementation of a range of different evidence-based interventions within clinical, community and other service settings. Great progress has come in extending the evidence base to support TA's use across multiple contexts, the result of more extensive categorizing of implementation strategies to support systematic studies of their effectiveness in facilitating successful implementation. This commentary builds on that progress to suggest several opportunities for future investigation and collaborative activity among researchers, practitioners, policymakers and other key decision-makers in hopes of continuing to build the success highlighted in this special issue and elsewhere. Authors call for increased attention to operationalization and tailoring of TA, considering how TA services can be sustained over time and how to consider externally-provided TA versus that housed within an organization. In addition, the commentary suggests a few key areas for capacity-building that can increase the quality, reach, and impact of TA for the future.

Background The field of implementation science has significantly expanded in size and scope over the past two decades, although work related to understanding implementation processes have of course long preceded the more systematic efforts to improve integration of evidence-based interventions into practice settings. While this growth has had significant benefits to research, practice, and policy, there are some clear challenges that this period of adolescence has uncovered. Main body This invited commentary reflects on the development of implementation science, its rapid growth, and milestones in its establishment as a viable component of the biomedical research enterprise. The authors reflect on progress in research and training, and then unpack some of the consequences of rapid growth, as the field has grappled with the competing challenges of legitimacy among the research community set against the necessary integration and engagement with practice and policy partners. The article then enumerates a set of principles for the field's next developmental stage and espouses the aspirational goal of a “big tent” to support the next generation of impactful science. Conclusion For implementation science to expand its relevance and impact to practice and policy, researchers must not lose sight of the original purpose of the field—to support improvements in health and health care at scale, the importance of building a community of research and practice among key partners, and the balance of rigor, relevance, and societal benefit.

Abstract Fifteen to 20 years is how long it takes for the billions of dollars of health-related research to translate into evidence-based policies and programs suitable for public use. Over the past two decades, an exciting science has emerged that seeks to narrow the gap between the discovery of new knowledge and its application in public health, mental health, and healthcare settings. Dissemination and implementation (D&I) research seeks to understand how to best apply scientific advances in the real world by focusing on pushing the evidence-based knowledge base into routine use. To help propel this crucial field forward, leading D&I scholars and researchers have collaborated to put together this volume to address a number of key issues, including how to evaluate the evidence base on effective interventions; which strategies will produce the greatest impact; how to design an appropriate study; and how to track a set of essential outcomes. D&I studies must also take into account the barriers to uptake of evidence-based interventions in the communities where people live their lives and the social service agencies, hospitals, and clinics where they receive care. The challenges of moving research to practice and policy are universal, and future progress calls for collaborative partnerships and cross-country research. The fundamental tenet of D&I research—taking what we know about improving health and putting it into practice—must be the highest priority. This book is a roadmap that will have broad appeal to researchers and practitioners across many disciplines.

Abstract There are tangible benefits to the use of theories, models, and frameworks (TMFs) to inform dissemination and implementation (D&I) research. However, D&I scientists may find it difficult to select, adapt, combine, and apply a specific TMF to their work. Guidance is provided on how to select a TMF because answering several questions (e.g., the research question, scope of the study) can aid a research team in selecting a TMF. A case study example of adapting a TMF to better address health equity is also included since, for nearly all studies, the selected TMF will need adaptation. Given the large number of TMFs available and the amount of work required to develop a new TMF, a researcher likely does not need to create a new TMF. An alternative might be to look outside the field of health (e.g., economics, political science, engineering) to identify other TMFs that could inform research as reviews have identified gaps in availability of TMFs for certain types of D&I research (e.g., economics, health equity, sustainability). The application and ongoing testing of theory-based approaches will increase the ability to ensure essential concepts are considered, enhance interpretability, support evaluation of outcome variations, and move the science forward.

With the launch of the Sustainable Development Goals (SDGs) in 2015, global leaders committed to the health and wellbeing of every person on the planet by 2030. With the development of numerous life-saving and life-enhancing innovations, the potential for using science and technology to achieve this goal has never been greater. Yet with far too many innovations there are stark and unacceptable inequities in availability and access. Further, a high proportion of effective interventions are not being put into practice effectively at scale, particularly in low-income and middle-income countries (LMICs) where scalability and sustainability of interventions with quality have been especially challenging.

The Cancer Prevention and Control Research Network (CPCRN) was established in 2002 to conduct applied research and undertake related activities to translate evidence into practice, with a special focus on the unmet needs of populations at higher risk of getting cancer and dying from it. A network of academic, public health and community partners, CPCRN is a thematic research network of the Prevention Research Centers Program at the Centers for Disease Control and Prevention (CDC). The National Cancer Institute's Division of Cancer Control and Population Sciences (DCCPS) has been a consistent collaborator. The CPCRN has fostered research on geographically dispersed populations through cross-institution partnerships across the network. Since its inception, the CPCRN has applied rigorous scientific methods to fill knowledge gaps in the application and implementation of evidence-based interventions, and it has developed a generation of leading investigators in the dissemination and implementation of effective public health practices. This article reflects on how CPCRN addressed national priorities, contributed to CDC's programs, emphasized health equity and impacted science over the past twenty years and potential future directions.

Over the past two decades, pragmatic and implementation science clinical trial research methods have advanced substantially. Pragmatic and implementation studies have natural areas of overlap, particularly relating to the goal of using clinical trial data to leverage health care system policy changes. Few investigations have addressed pragmatic and implementation science randomized trial methods development while also considering policy impact. The investigation used the PRagmatic Explanatory Continuum Indicator Summary-2 (PRECIS-2) and PRECIS-2-Provider Strategies (PRECIS-2-PS) tools to evaluate the design of two multisite randomized clinical trials that targeted patient-level effectiveness outcomes, provider-level practice changes and health care system policy. Seven raters received PRECIS-2 training and applied the tools in the coding of the two trials. Descriptive statistics were produced for both trials, and PRECIS-2 wheel diagrams were constructed. Interrater agreement was assessed with the Intraclass Correlation (ICC) and Kappa statistics. The Rapid Assessment Procedure Informed Clinical Ethnography (RAPICE) qualitative approach was applied to understanding integrative themes derived from the PRECIS-2 ratings and an end-of-study policy summit. The ICCs for the composite ratings across the patient and provider-focused PRECIS-2 domains ranged from 0.77 to 0.87, and the Kappa values ranged from 0.25 to 0.37, reflecting overall fair-to-good interrater agreement for both trials. All four PRECIS-2 wheels were rated more pragmatic than explanatory, with composite mean and median scores ≥ 4. Across trials, the primary intent-to-treat analysis domain was consistently rated most pragmatic (mean = 5.0, SD = 0), while the follow-up/data collection domain was rated most explanatory (mean range = 3.14–3.43, SD range = 0.49–0.69). RAPICE field notes identified themes related to potential PRECIS-2 training improvements, as well as policy themes related to using trial data to inform US trauma care system practice change; the policy themes were not captured by the PRECIS-2 ratings. The investigation documents that the PRECIS-2 and PRECIS-2-PS can be simultaneously used to feasibly and reliably characterize clinical trials with patient and provider-level targets. The integration of pragmatic and implementation science clinical trial research methods can be furthered by using common metrics such as the PRECIS-2 and PRECIS-2-PS. Future study could focus on clinical trial policy research methods development. DO-SBIS ClinicalTrials.gov NCT00607620. registered on January 29, 2008. TSOS ClinicalTrials.gov NCT02655354, registered on July 27, 2015.

The vision of the Central Society for Clinical and Translational Research (CSCTR) is to "promote a vibrant, supportive community of multidisciplinary, clinical, and translational medical research to benefit humanity." Together with the Midwestern Section of the American Federation for Medical Research, CSCTR hosts an Annual Midwest Clinical & Translational Research Meeting, a regional multispecialty meeting that provides the opportunity for trainees and early-stage investigators to present their research to leaders in their fields. There is an increasing national and global interest in implementation science (IS), the systematic study of activities (or strategies) to facilitate the successful uptake of evidence-based health interventions in clinical and community settings. Given the growing importance of this field and its relevance to the goals of the CSCTR, in 2022, the Midwest Clinical & Translational Research Meeting incorporated new initiatives and sessions in IS. In this report, we describe the role of IS in the translational research spectrum, provide a summary of sessions from the 2022 Midwest Clinical & Translational Research Meeting, and highlight initiatives to complement national efforts to build capacity for IS through the annual meetings.

BACKGROUND:Conducting an embedded pragmatic clinical trial in the workflow of a healthcare system is a complex endeavor. The complexity of the intervention delivery can have implications for study planning, ability to maintain fidelity to the intervention during the trial, and/or ability to detect meaningful differences in outcomes. METHODS:We conducted a literature review, developed a tool, and conducted two rounds of phone calls with NIH Pragmatic Trials Collaboratory Demonstration Project principal investigators to develop the Intervention Delivery Complexity Tool. After refining the tool, we piloted it with Collaboratory demonstration projects and developed an online version of the tool using the R Shiny application (https://duke-som.shinyapps.io/ICT-ePCT/). RESULTS:The 6-item tool consists of internal and external factors. Internal factors pertain to the intervention itself and include workflow, training, and the number of intervention components. External factors are related to intervention delivery at the system level including differences in healthcare systems, the dependency on setting for implementation, and the number of steps between the intervention and the outcome. CONCLUSION:The Intervention Delivery Complexity Tool was developed as a standard way to overcome communication challenges of intervention delivery within an embedded pragmatic trial. This version of the tool is most likely to be useful to the trial team and its health system partners during trial planning and conduct. We expect further evolution of the tool as more pragmatic trials are conducted and feedback is received on its performance outside of the NIH Pragmatic Trials Collaboratory.

Abstract Background Chronic disease management (CDM) through sustained knowledge translation (KT) interventions ensures long-term, high-quality care. We assessed implementation of KT interventions for supporting CDM and their efficacy when sustained in older adults. Methods Design: Systematic review with meta-analysis engaging 17 knowledge users using integrated KT. Eligibility criteria: Randomized controlled trials (RCTs) including adults (> 65 years old) with chronic disease(s), their caregivers, health and/or policy-decision makers receiving a KT intervention to carry out a CDM intervention for at least 12 months (versus other KT interventions or usual care). Information sources: We searched MEDLINE, EMBASE, and the Cochrane Central Register of Controlled Trials from each database’s inception to March 2020. Outcome measures: Sustainability, fidelity, adherence of KT interventions for CDM practice, quality of life (QOL) and quality of care (QOC). Data extraction, risk of bias (ROB) assessment: We screened, abstracted and appraised articles (Effective Practice and Organisation of Care ROB tool) independently and in duplicate. Data synthesis: We performed both random-effects and fixed-effect meta-analyses and estimated mean differences (MDs) for continuous and odds ratios (ORs) for dichotomous data. Results We included 158 RCTs (973,074 participants [961,745 patients, 5540 caregivers, 5789 providers]) and 39 companion reports comprising 329 KT interventions, involving patients (43.2%), healthcare providers (20.7%) or both (10.9%). We identified 16 studies described as assessing sustainability in 8.1% interventions, 67 studies as assessing adherence in 35.6% interventions and 20 studies as assessing fidelity in 8.7% of the interventions. Most meta-analyses suggested that KT interventions improved QOL, but imprecisely (36 item Short-Form mental [SF-36 mental]: MD 1.11, 95% confidence interval [CI] [− 1.25, 3.47], 14 RCTs, 5876 participants, I2 = 96%; European QOL-5 dimensions: MD 0.01, 95% CI [− 0.01, 0.02], 15 RCTs, 6628 participants, I2 = 25%; St George’s Respiratory Questionnaire: MD − 2.12, 95% CI [− 3.72, − 0.51] 44 12 RCTs, 2893 participants, I2 = 44%). KT interventions improved QOC (OR 1.55, 95% CI [1.29, 1.85], 12 RCTS, 5271 participants, I2 = 21%). Conclusions KT intervention sustainability was infrequently defined and assessed. Sustained KT interventions have the potential to improve QOL and QOC in older adults with CDM. However, their overall efficacy remains uncertain and it varies by effect modifiers, including intervention type, chronic disease number, comorbidities, and participant age. Systematic review registration PROSPERO CRD42018084810.

Rationale The 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 study Experiences 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 policy Updating 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 implications Real-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.

While the recognition of the need to adapt interventions to improve their fit with populations and service systems has been well established within the scientific community, limited consideration of the role of adaptation within implementation science has impeded progress toward optimal uptake of evidence-based care. This article reflects on the traditional paths through which adapted interventions were studies, progress made in recent years toward better integration of the science of adaptation within implementation studies with reference to a special publication series, and next steps for the field to continue to build a robust knowledge base on adaptation.

The National Cancer Institute's Implementation Science Centers in Cancer Control (ISC3) Network represents a large-scale initiative to create an infrastructure to support and enable the efficient, effective, and equitable translation of approaches and evidence-based treatments to reduce cancer risk and improve outcomes. This Cancer MoonshotSM-funded ISC3 Network consists of 7 P50 Centers that support and advance the rapid development, testing, and refinement of innovative approaches to implement a range of evidence-based cancer control interventions. The Centers were designed to have research-practice partnerships at their core and to create the opportunity for a series of pilot studies that could explore new and sometimes risky ideas and embed in their infrastructure a 2-way engagement and collaboration essential to stimulating lasting change. ISC3 also seeks to enhance capacity of researchers, practitioners, and communities to apply implementation science approaches, methods, and measures. The Organizing Framework that guides the work of ISC3 highlights a collective set of 3 core areas of collaboration within and among Centers, including to 1) assess and incorporate dynamic, multilevel context; 2) develop and conduct rapid and responsive pilot and methods studies; and 3) build capacity for knowledge development and exchange. Core operating principles that undergird the Framework include open collaboration, consideration of the dynamic context, and engagement of multiple implementation partners to advance pragmatic methods and health equity and facilitate leadership and capacity building across implementation science and cancer control.

Cancer prevention and control research has produced a variety of effective interventions over the years, though most are single disease focused. To meet the Cancer Moonshot goal to reduce the cancer death rate by 50% by 2047, it may be necessary to overcome the limitations of siloed interventions that do not meet people's multiple needs and limitations in system capacity to deliver the increasing number of interventions in parallel. In this article, we propose integrating multiple evidence-based interventions as a potential solution. We define 2 types of integrated interventions, blended and bundled, and provide examples to illustrate each. We then offer a schematic and outline considerations for how to assemble blended or bundled interventions including looking at the intervention need or opportunity along the cancer continuum as well as co-occurring behaviors or motivations. We also discuss delivery workflow integration considerations including social-ecological level(s), context or setting, implementer, and intended beneficiary. Finally, in assembling integrated interventions, we encourage consideration of practice-based expertise and community and/or patient input. After assembly, we share thoughts related to implementation and evaluation of blended or bundled interventions. To conclude the article, we present multiple research opportunities in this space. With swift progress on these research directions, cancer prevention and control interventionists and implementation scientists can contribute to achieving the promise of the reignited Cancer Moonshot.

The 17-year time span between discovery and application of evidence in practice has become a unifying challenge for implementation science and translational science more broadly. Further, global pandemics and social crises demand timely implementation of rapidly accruing evidence to reduce morbidity and mortality. Yet speed remains an understudied metric in implementation science. Prevailing evaluations of implementation lack a temporal aspect, and current approaches have not yielded rapid implementation. In this paper, we address speed as an important conceptual and methodological gap in implementation science. We aim to untangle the complexities of studying implementation speed, offer a framework to assess speed of translation (FAST), and provide guidance to measure speed in evaluating implementation. To facilitate specification and reporting on metrics of speed, we encourage consideration of stakeholder perspectives (e.g., comparison of varying priorities), referents (e.g., speed in attaining outcomes, transitioning between implementation phases), and observation windows (e.g., time from intervention development to first patient treated) in its measurement. The FAST framework identifies factors that may influence speed of implementation and potential effects of implementation speed. We propose a research agenda to advance understanding of the pace of implementation, including identifying accelerators and inhibitors to speed.

This article provides new reflections and recommendations from authors of the initial effectiveness-implementation hybrid study manuscript and additional experts in their conceptualization and application. Given the widespread and continued use of hybrid studies, critical appraisals are necessary. The article offers reflections across five conceptual and methodological areas. It begins with the recommendation to replace the term “design” in favor of “study.” The use of the term “design” and the explicit focus on trial methodology in the original paper created confusion. The essence of hybrid studies is combining research questions concerning intervention effectiveness and implementation within the same study, and this can and should be achieved by applying a full range of research designs. Supporting this recommendation, the article then offers guidance on selecting a hybrid study type based on evidentiary and contextual information and stakeholder concerns/preferences. A series of questions are presented that have been designed to help investigators select the most appropriate hybrid type for their study situation. The article also provides a critique on the hybrid 1-2-3 typology and offers reflections on when and how to use the typology moving forward. Further, the article offers recommendations on research designs that align with each hybrid study type. Lastly, the article offers thoughts on how to integrate costs analyses into hybrid studies.