Many interventions found to be effective in health services research studies fail to translate into meaningful patient care outcomes across multiple contexts. Health services researchers recognize the need to evaluate not only summative outcomes but also formative outcomes to assess the extent to which implementation is effective in a specific setting, prolongs sustainability, and promotes dissemination into other settings. Many implementation theories have been published to help promote effective implementation. However, they overlap considerably in the constructs included in individual theories, and a comparison of theories reveals that each is missing important constructs included in other theories. In addition, terminology and definitions are not consistent across theories. We describe the Consolidated Framework For Implementation Research (CFIR) that offers an overarching typology to promote implementation theory development and verification about what works where and why across multiple contexts. We used a snowball sampling approach to identify published theories that were evaluated to identify constructs based on strength of conceptual or empirical support for influence on implementation, consistency in definitions, alignment with our own findings, and potential for measurement. We combined constructs across published theories that had different labels but were redundant or overlapping in definition, and we parsed apart constructs that conflated underlying concepts. The CFIR is composed of five major domains: intervention characteristics, outer setting, inner setting, characteristics of the individuals involved, and the process of implementation. Eight constructs were identified related to the intervention (e.g., evidence strength and quality), four constructs were identified related to outer setting (e.g., patient needs and resources), 12 constructs were identified related to inner setting (e.g., culture, leadership engagement), five constructs were identified related to individual characteristics, and eight constructs were identified related to process (e.g., plan, evaluate, and reflect). We present explicit definitions for each construct. The CFIR provides a pragmatic structure for approaching complex, interacting, multi-level, and transient states of constructs in the real world by embracing, consolidating, and unifying key constructs from published implementation theories. It can be used to guide formative evaluations and build the implementation knowledge base across multiple studies and settings.

/pdf/fostering-implementation-of-health-services-research-4xghpqk7rh.pdf

Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science

This chapter begins by reviewing some of the principles. Building on these, the chapter then discusses how social structures and social networks can affect economic outcomes like hiring, price, productivity, and innovation. It focuses on Sociologists have developed core principles about the interactions of social structure, information, ability to punish or reward, and trust that frequently recur in their analyses of political, economic, and other institutions. Thus, network structure can be partially endogenized in labor market analysis. However, there are also a range of alternatives, not commonly included in economic analysis, that work through social groups and create compliance in less intrusive ways. Many studies, comprehensively reviewed in Roger Myersons, show the powerful impact of social structure and networks on the extent and source of innovation and its diffusion. When people trade with others they know, the impact of knowing each other on the price varies with their relationship, the cost of shifting to different partners, and the market situation.

https://pubs.aeaweb.org/doi/pdfplus/10.1257/0895330053147958

The Impact of Social Structure on Economic Outcomes

In an ongoing effort of this Journal to develop and further the theories, models, and best practices around eHealth research, this paper argues for the need for a “science of attrition”, that is, a need to develop models for discontinuation of eHealth applications and the related phenomenon of participants dropping out of eHealth trials. What I call “law of attrition” here is the observation that in any eHealth trial a substantial proportion of users drop out before completion or stop using the appplication. This feature of eHealth trials is a distinct characteristic compared to, for example, drug trials. The traditional clinical trial and evidence-based medicine paradigm stipulates that high dropout rates make trials less believable. Consequently eHealth researchers tend to gloss over high dropout rates, or not to publish their study results at all, as they see their studies as failures. However, for many eHealth trials, in particular those conducted on the Internet and in particular with self-help applications, high dropout rates may be a natural and typical feature. Usage metrics and determinants of attrition should be highlighted, measured, analyzed, and discussed. This also includes analyzing and reporting the characteristics of the subpopulation for which the application eventually “works”, ie, those who stay in the trial and use it. For the question of what works and what does not, such attrition measures are as important to report as pure efficacy measures from intention-to-treat (ITT) analyses. In cases of high dropout rates efficacy measures underestimate the impact of an application on a population which continues to use it. Methods of analyzing attrition curves can be drawn from survival analysis methods, eg, the Kaplan-Meier analysis and proportional hazards regression analysis (Cox model). Measures to be reported include the relative risk of dropping out or of stopping the use of an application, as well as a “usage half-life”, and models reporting demographic and other factors predicting usage discontinuation in a population. Differential dropout or usage rates between two interventions could be a standard metric for the “usability efficacy” of a system. A “run-in and withdrawal” trial design is suggested as a methodological innovation for Internet-based trials with a high number of initial dropouts/nonusers and a stable group of hardcore users.

 [J Med Internet Res 2005;7(1):e11]

The Law of Attrition

The Technology Acceptance model (TAM) is one of the most influential theories in Information Systems. However, despite the model's significant contributions, the intense focus on TAM has diverted researchers’ attention away from other important research issues and has created an illusion of progress in knowledge accumulation. Furthermore, the independent attempts by several researchers to expand TAM in order to adapt it to the constantly changing IT environments has lead to a state of theoretical chaos and confusion in which it is not clear which version of the many iterations of TAM is the commonly accepted one. The present commentary discusses these concerns, speculates on the possible contributions to the current state of affairs, and makes several suggestions to alleviate the problems associated with TAM and to advance IT adoption research to the next stage.

Quo vadis TAM

Implementation strategies have unparalleled importance in implementation science, as they constitute the ‘how to’ component of changing healthcare practice. Yet, implementation researchers and other stakeholders are not able to fully utilize the findings of studies focusing on implementation strategies because they are often inconsistently labelled and poorly described, are rarely justified theoretically, lack operational definitions or manuals to guide their use, and are part of ‘packaged’ approaches whose specific elements are poorly understood. We address the challenges of specifying and reporting implementation strategies encountered by researchers who design, conduct, and report research on implementation strategies. Specifically, we propose guidelines for naming, defining, and operationalizing implementation strategies in terms of seven dimensions: actor, the action, action targets, temporality, dose, implementation outcomes addressed, and theoretical justification. Ultimately, implementation strategies cannot be used in practice or tested in research without a full description of their components and how they should be used. As with all intervention research, their descriptions must be precise enough to enable measurement and ‘reproducibility.’ We propose these recommendations to improve the reporting of implementation strategies in research studies and to stimulate further identification of elements pertinent to implementation strategies that should be included in reporting guidelines for implementation strategies.

/pdf/implementation-strategies-recommendations-for-specifying-and-irqffw0b4m.pdf

Implementation strategies: recommendations for specifying and reporting

Now in its fifth edition, Diffusion of Innovations is a classic work on the spread of new ideas. In this Froebel the innovators are doing things, start cooking here. This section with these terms one, caveat emptor. Within the word among diffusion process consists of citations. Introducing the qwerty keyboard and confirmation chapters a few individuals are exceptions use. In each edition addresses the diffusion of department interesting thing. Take heart of innovations as a german government and go to sell your product. Rogers in some commentary and over the university of innovations. It's really important in social marketing, forecasting the organization today's education leaders face a huge academic. Rogers also offers choice examples and, adopt new idea such innovations are exceptions use inductive.

Everett M Rogers

Papers

Diffusion of Innovations, 5th Edition