Moving From Research to Large-Scale Change in Child Health Care
Department of Pediatrics, Stanford University School of Medicine, Palo Alto, California , USA. Academic pediatrics
(Impact Factor: 2.01).
07/2011; 11(5):360-8. DOI: 10.1016/j.acap.2011.06.004
There is a large and persistent failure to achieve widespread dissemination of evidence-based practices in child health care. Too often studies demonstrating evidence for effective child health care practices are not brought to scale and across different settings and populations. This failure is not due to a lack of knowledge, but rather a failure to bring to bear proven methods in dissemination, diffusion, and implementation (DD&I) science that target the translation of evidence-based medicine to everyday practice. DD&I science offers a framework and a set of tools to identify innovations that are likely to be implemented, and provides methods to better understand the capabilities and preferences of individuals and organizations and the social networks within these organizations that help facilitate widespread adoption. Successful DD&I is dependent on making the intervention context sensitive without losing fidelity to the core components of the intervention. The achievement of these goals calls for new research methods such as pragmatic research trials that combine hypothesis testing with quality improvement, participatory research that engages the target community at the beginning of research design, and other quasi-experimental designs. With the advent of health care reform, it will be extremely important to ensure that the ensuing large demonstration projects that are designed to increase integrated care and better control costs can be rapidly brought to scale across different practices settings, and health plans and will be able to achieve effectiveness in diverse populations.
Available from: implementationscience.com
- "The literature on achieving results at scale describes various approaches, taking into account factors at the smallest scale, including details of the intervention itself ; factors at the largest scale, including the larger socio-political and economic context; and myriad factors in between, including variables related to the implementing health systems, communities, and practitioners567891011. This approach accommodates multi-level interventions that address the complexities of the environment and interacting systems. "
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ABSTRACT: Background Scaling up complex health interventions to large populations is not a straightforward task. Without intentional, guided efforts to scale up, it can take many years for a new evidence-based intervention to be broadly implemented. For the past decade, researchers and implementers have developed models of scale-up that move beyond earlier paradigms that assumed ideas and practices would successfully spread through a combination of publication, policy, training, and example. Drawing from the previously reported frameworks for scaling up health interventions and our experience in the USA and abroad, we describe a framework for taking health interventions to full scale, and we use two large-scale improvement initiatives in Africa to illustrate the framework in action. We first identified other scale-up approaches for comparison and analysis of common constructs by searching for systematic reviews of scale-up in health care, reviewing those bibliographies, speaking with experts, and reviewing common research databases (PubMed, Google Scholar) for papers in English from peer-reviewed and “gray” sources that discussed models, frameworks, or theories for scale-up from 2000 to 2014. We then analyzed the results of this external review in the context of the models and frameworks developed over the past 20 years by Associates in Process Improvement (API) and the Institute for Healthcare improvement (IHI). Finally, we reflected on two national-scale improvement initiatives that IHI had undertaken in Ghana and South Africa that were testing grounds for early iterations of the framework presented in this paper. Results The framework describes three core components: a sequence of activities that are required to get a program of work to full scale, the mechanisms that are required to facilitate the adoption of interventions, and the underlying factors and support systems required for successful scale-up. The four steps in the sequence include (1) Set-up, which prepares the ground for introduction and testing of the intervention that will be taken to full scale; (2) Develop the Scalable Unit, which is an early testing phase; (3) Test of Scale-up, which then tests the intervention in a variety of settings that are likely to represent different contexts that will be encountered at full scale; and (4) Go to Full Scale, which unfolds rapidly to enable a larger number of sites or divisions to adopt and/or replicate the intervention. Conclusions Our framework echoes, amplifies, and systematizes the three dominant themes that occur to varying extents in a number of existing scale-up frameworks. We call out the crucial importance of defining a scalable unit of organization. If a scalable unit can be defined, and successful results achieved by implementing an intervention in this unit without major addition of resources, it is more likely that the intervention can be fully and rapidly scaled. When tying this framework to quality improvement (QI) methods, we describe a range of methodological options that can be applied to each of the four steps in the framework’s sequence.
Available from: Frances Clare Cunningham
- "Translational research is considered a priority in the research agenda of many countries for example the nine Collaboratives for Leadership in Applied Health Research & Care (CLAHRCs) in England and the large number of interorganisational alliances funded by the Clinical and Translation Science Awards in the US. Collaboration [35-38], innovation [39-41], knowledge exchange [36,42] and diffusion of findings [43-45] are all intended outcomes of TRNs. Key players have been associated with all these functions in a range of network settings [11,19,26,46-48]. "
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ABSTRACT: Professional networks are used increasingly in health care to bring together members from different sites and professions to work collaboratively. Key players within these networks are known to affect network function through their central or brokerage position and are therefore of interest to those who seek to optimise network efficiency. However, their identity may not be apparent. This study using social network analysis to ask: (1) Who are the key players of a new translational research network (TRN)? (2) Do they have characteristics in common? (3) Are they recognisable as powerful, influential or well connected individuals?
TRN members were asked to complete an on-line, whole network survey which collected demographic information expected to be associated with key player roles, and social network questions about collaboration in current TRN projects. Three questions asked who they perceived as powerful, influential and well connected. Indegree and betweenness centrality values were used to determine key player status in the actual and perceived networks and tested for association with demographic and descriptive variables using chi square analyses.
Response rate for the online survey was 76.4% (52/68). The TRN director and manager were identified as key players along with six other members. Only two of nine variables were associated with actual key player status; none with perceived. The main finding was the mismatch between actual and perceived brokers. Members correctly identified two of the three central actors (the two mandated key roles director and manager) but there were only three correctly identified actual brokers among the 19 perceived brokers. Possible reasons for the mismatch include overlapping structures and weak knowledge of members.
The importance of correctly identifying these key players is discussed in terms of network interventions to improve efficiency.
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