De-implementing inappropriate health interventions is essential for minimizing patient harm, maximizing efficient use of resources, and improving population health. Research on de-implementation has ...expanded in recent years as it cuts across types of interventions, patient populations, health conditions, and delivery settings. This commentary explores unique aspects of de-implementing inappropriate interventions that differentiate it from implementing evidence-based interventions, including multi-level factors, types of action, strategies for de-implementation, outcomes, and unintended negative consequences. We highlight opportunities to continue to advance research on the de-implementation of inappropriate interventions in health care and public health.
In the past few decades, prevention scientists have developed and tested a range of interventions with demonstrated benefits on child and adolescent cognitive, affective, and behavioral health. These ...evidence-based interventions offer promise of population-level benefit if accompanied by findings of implementation science to facilitate adoption, widespread implementation, and sustainment. Though there have been notable examples of successful efforts to scale up interventions, more work is needed to optimize benefit. Although the traditional pathway from intervention development and testing to implementation has served the research community well-allowing for a systematic advance of evidence-based interventions that appear ready for implementation-progress has been limited by maintaining the hypothesis that evidence generation must be complete prior to implementation. This sets up the challenging dichotomy between fidelity and adaptation and limits the science of adaptation to findings from randomized trials of adapted interventions. The field can do better. This paper argues for the development of strategies to advance the science of adaptation in the context of implementation that would more comprehensively describe the needed fit between interventions and their settings, and embrace opportunities for ongoing learning about optimal intervention delivery over time. Efforts to build the resulting adaptome (pronounced "adapt-ohm") will include the construction of a common data platform to house systematically captured information about variations in delivery of evidence-based interventions across multiple populations and contexts, and provide feedback to intervention developers, as well as the implementation research and practice communities. Finally, the article identifies next steps to jumpstart adaptome data platform development.
Glucagon-like peptide 1 (GLP-1) is necessary for normal gluco-regulation, and it has been widely presumed that this function reflects the actions of GLP-1 released from enteroendocrine L cells. To ...test the relative importance of intestinal versus pancreatic sources of GLP-1 for physiological regulation of glucose, we administered a GLP-1R antagonist, exendin-9-39 (Ex9), to mice with tissue-specific reactivation of the preproglucagon gene (Gcg). Ex9 impaired glucose tolerance in wild-type mice but had no impact on Gcg-null or GLP-1R KO mice, suggesting that Ex9 is a true and specific GLP-1R antagonist. Unexpectedly, Ex-9 had no effect on blood glucose in mice with restoration of intestinal Gcg. In contrast, pancreatic reactivation of Gcg fully restored the effect of Ex9 to impair both oral and i.p. glucose tolerance. These findings suggest an alternative model whereby islet GLP-1 also plays an important role in regulating glucose homeostasis.
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•Intestinally secreted GLP-1 is presumed to regulate glucose via incretin action•Exendin-9 does not alter glucose in mice that only produce GLP-1 in the intestine•Exendin-9 does impair glucose in mice that only produce GLP-1 in the pancreas•Alternative to the incretin model, islet GLP-1 is crucial for gluco-regulation
GLP-1 is necessary for normal gluco-regulation, and it has been widely presumed that this function is the action of peptide released from enteroendocrine L cells. The data from Chambers et al. challenge this dogma and find that intestinally produced GLP-1 is dispensable, while pancreatic production of GLP-1 is necessary for gluco-regulation.
Theories and frameworks (hereafter called models) enhance dissemination and implementation (D&I) research by making the spread of evidence-based interventions more likely. This work organizes and ...synthesizes these models by (1) developing an inventory of models used in D&I research; (2) synthesizing this information; and (3) providing guidance on how to select a model to inform study design and execution.
This review began with commonly cited models and model developers and used snowball sampling to collect models developed in any year from journal articles, presentations, and books. All models were analyzed and categorized in 2011 based on three author-defined variables: construct flexibility, focus on dissemination and/or implementation activities (D/I), and the socioecologic framework (SEF) level. Five-point scales were used to rate construct flexibility from broad to operational and D/I activities from dissemination-focused to implementation-focused. All SEF levels (system, community, organization, and individual) applicable to a model were also extracted. Models that addressed policy activities were noted.
Sixty-one models were included in this review. Each of the five categories in the construct flexibility and D/I scales had at least four models. Models were distributed across all levels of the SEF; the fewest models (n=8) addressed policy activities. To assist researchers in selecting and utilizing a model throughout the research process, the authors present and explain examples of how models have been used.
These findings may enable researchers to better identify and select models to inform their D&I work.
RE-AIM is a widely adopted, robust implementation science (IS) framework used to inform intervention and implementation design, planning, and evaluation, as well as to address short-term maintenance. ...In recent years, there has been growing focus on the longer-term sustainability of evidence-based programs, policies and practices (EBIs). In particular, investigators have conceptualized sustainability as the continued health impact and delivery of EBIs over a longer period of time (e.g., years after initial implementation) and incorporated the complex and evolving nature of context. We propose a reconsideration of RE-AIM to integrate recent conceptualizations of sustainability with a focus on addressing dynamic context and promoting health equity. In this Perspective, we present an extension of the RE-AIM framework to guide planning, measurement/evaluation, and adaptations focused on enhancing sustainability. We recommend consideration of: (1) extension of "maintenance" within RE-AIM to include recent conceptualizations of dynamic, longer-term intervention sustainability and "evolvability" across the life cycle of EBIs, including adaptation and potential de-implementation in light of changing and evolving evidence, contexts, and population needs; (2) iterative application of RE-AIM assessments to guide adaptations and enhance long-term sustainability; (3) explicit consideration of equity and cost as fundamental, driving forces that need to be addressed across RE-AIM dimensions to enhance sustainability; and (4) use or integration of RE-AIM with other existing frameworks that address key contextual factors and examine multi-level determinants of sustainability. Finally, we provide testable hypotheses and detailed research questions to inform future research in these areas.
Despite growth in implementation research, limited scientific attention has focused on understanding and improving sustainability of health interventions. Models of sustainability have been evolving ...to reflect challenges in the fit between intervention and context.
We examine the development of concepts of sustainability, and respond to two frequent assumptions -'voltage drop,' whereby interventions are expected to yield lower benefits as they move from efficacy to effectiveness to implementation and sustainability, and 'program drift,' whereby deviation from manualized protocols is assumed to decrease benefit. We posit that these assumptions limit opportunities to improve care, and instead argue for understanding the changing context of healthcare to continuously refine and improve interventions as they are sustained. Sustainability has evolved from being considered as the endgame of a translational research process to a suggested 'adaptation phase' that integrates and institutionalizes interventions within local organizational and cultural contexts. These recent approaches locate sustainability in the implementation phase of knowledge transfer, but still do not address intervention improvement as a central theme. We propose a Dynamic Sustainability Framework that involves: continued learning and problem solving, ongoing adaptation of interventions with a primary focus on fit between interventions and multi-level contexts, and expectations for ongoing improvement as opposed to diminishing outcomes over time.
A Dynamic Sustainability Framework provides a foundation for research, policy and practice that supports development and testing of falsifiable hypotheses and continued learning to advance the implementation, transportability and impact of health services research.
Since the start of the Human Genome Project 25 years ago, basic discoveries related to genomics and other "-omic" fields have continued to advance exponentially. This progress has facilitated the ...2015 launch of the US Precision Medicine Initiative (PMI). The PMI is intended to merge genomic, biological, behavioral, environmental, and other data on individuals to identify drivers of health that might support personalized health care decision making. In the cancer domain, for example, recognition of both inherited genetic susceptibility (eg, Lynch syndrome for colorectal cancer, and BRCA1/2 for breast cancer) and cancer genome sequence alterations that can pinpoint therapeutic agents (eg, National Cancer Institute's MATCH trials) has the potential to make clinical decisions more personalized both in prevention and treatment. The "National Cancer Moonshot Initiative" seeks to rapidly scale up these efforts. Here, Chambers et al discuss convergence of implementation science, precision medicine, and the learning health care system, a new model for biomedical research.
Understanding the mechanisms of implementation strategies (i.e., the processes by which strategies produce desired effects) is important for research to understand why a strategy did or did not ...achieve its intended effect, and it is important for practice to ensure strategies are designed and selected to directly target determinants or barriers. This study is a systematic review to characterize how mechanisms are conceptualized and measured, how they are studied and evaluated, and how much evidence exists for specific mechanisms.
We systematically searched PubMed and CINAHL Plus for implementation studies published between January 1990 and August 2018 that included the terms "mechanism," "mediator," or "moderator." Two authors independently reviewed title and abstracts and then full texts for fit with our inclusion criteria of empirical studies of implementation in health care contexts. Authors extracted data regarding general study information, methods, results, and study design and mechanisms-specific information. Authors used the Mixed Methods Appraisal Tool to assess study quality.
Search strategies produced 2277 articles, of which 183 were included for full text review. From these we included for data extraction 39 articles plus an additional seven articles were hand-entered from only other review of implementation mechanisms (total = 46 included articles). Most included studies employed quantitative methods (73.9%), while 10.9% were qualitative and 15.2% were mixed methods. Nine unique versions of models testing mechanisms emerged. Fifty-three percent of the studies met half or fewer of the quality indicators. The majority of studies (84.8%) only met three or fewer of the seven criteria stipulated for establishing mechanisms.
Researchers have undertaken a multitude of approaches to pursue mechanistic implementation research, but our review revealed substantive conceptual, methodological, and measurement issues that must be addressed in order to advance this critical research agenda. To move the field forward, there is need for greater precision to achieve conceptual clarity, attempts to generate testable hypotheses about how and why variables are related, and use of concrete behavioral indicators of proximal outcomes in the case of quantitative research and more directed inquiry in the case of qualitative research.
Gaps remain between the outcomes of biomedical research and their application within clinical and community settings. The field of implementation science, also referred to as dissemination and ...implementation research, is intended to improve the adoption, uptake, and sustainability of evidence-based health interventions. The articles in this volume's symposium on implementation science and public health identify important directions in the effort to maximize the impact of research on public and population health. Leading researchers present reviews of the use of quasi-experimental designs in implementation science, the movement toward enhancing evidence-based public health, and intervention sustainability. Each article presents lessons learned from prior research and recommendations for the next generation of studies. Collectively, the symposium offers a road map for future implementation science that seeks to optimize public health.
A number of commentaries have suggested that large studies are more reliable than smaller studies and there is a growing interest in the analysis of “big data” that integrates information from many ...thousands of persons and/or different data sources. We consider a variety of biases that are likely in the era of big data, including sampling error, measurement error, multiple comparisons errors, aggregation error, and errors associated with the systematic exclusion of information. Using examples from epidemiology, health services research, studies on determinants of health, and clinical trials, we conclude that it is necessary to exercise greater caution to be sure that big sample size does not lead to big inferential errors. Despite the advantages of big studies, large sample size can magnify the bias associated with error resulting from sampling or study design. Clin Trans Sci 2014; Volume #: 1–5
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