Moving From Research to Large-Scale Change in Child Health Care
ABSTRACT 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.
- Academic pediatrics 05/2013; 13(3):181-3. DOI:10.1016/j.acap.2013.03.012 · 2.23 Impact Factor
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ABSTRACT: Context: The long-term sustainability of whole-system change programs is rarely studied, and when it is, it is inevitably undertaken in a shifting context, thereby raising epistemological and methodological questions. This article describes a transferable methodology that was developed to guide the evaluation of a three-year follow-up of a large health care change program in London, which took place during a period of economic turbulence and rapid policy change. Method: Using a mixed-method organizational case study design, we studied three services (stroke, kidney, and sexual health) across primary and secondary care. Each had received £5 million (US$7.8 million) in modernization funding in 2004. In 2010/2011, we gathered data on the services and compared them with data from 2004 to 2008. The new data set contained quantitative statistics (access, process, and outcome metrics), qualitative interviews with staff and patients, documents, and field notes. Our data analysis was informed by two complementary models of sustainability: intervention-focused (guided by the question, What, if anything, of the original program has been sustained?) and system-dynamic (guided by the question, How and why did change unfold as it did in this complex system?). Findings: Some but not all services introduced in the original transformation effort of 2004-2008 were still running; others had ceased or been altered substantially to accommodate contextual changes (e.g., in case mix, commissioning priorities, or national policies). Key cultural changes (e.g., quality improvement, patient centeredness) largely persisted, and innovative ideas and practices had spread elsewhere. To draw causal links between the original program and current activities and outcomes, it was necessary to weave a narrative thread with multiple intervening influences. In particular, against a background of continuous change in the local health system, the sustainability of the original vision and capacity for quality improvement was strongly influenced by (1) stakeholders' conflicting and changing interpretations of the targeted health need; (2) changes in how the quality cycle was implemented and monitored; and (3) conflicts in stakeholders' values and what each stood to gain or lose. Conclusions: The sustainability of whole-system change embodies a tension between the persistence of past practice and the adaptation to a changing context. Although the intervention-focused question, What has persisted from the original program? (addressed via a conventional logic model), may be appropriate, evaluators should qualify their findings by also considering the system-dynamic question, What has changed, and why? (addressed by producing a meaningful narrative).Milbank Quarterly 09/2012; 90(3):516-47. DOI:10.1111/j.1468-0009.2012.00673.x · 5.06 Impact Factor
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ABSTRACT: More than half a million new items of biomedical research are generated every year and added to Medline. How successful are we at applying this steady accumulation of scientific knowledge and so improving the practice of medicine in the USA? The conventional wisdom is that the US healthcare system is plagued by serious cost, access, safety and quality weaknesses. A comprehensive solution must involve the better translation of an abundance of clinical research into improved clinical practice.Yet the application of knowledge (i.e. technology) remains far less well funded and less visible than the generation, synthesis and accumulation of knowledge (i.e. science), and the two are only weakly integrated. Worse, technology is often seen merely as an adjunct to practice, e.g. electronic health records.Several key changes are in order. A helpful first step lies in better understanding the distinction between science and technology, and their complementary strengths and limitations. The absolute level of funding for technology development must be increased as well as being more integrated with traditional science-based clinical research. In such a mission-oriented federal funding strategy, the ties between basic science research and applied research would be better emphasized and strengthened. It bears repeating that only by applying the wealth of existing and future scientific knowledge can healthcare delivery and patient care ever show significant improvement.BMC Medical Informatics and Decision Making 09/2012; 12:103. DOI:10.1186/1472-6947-12-103 · 1.50 Impact Factor