Comment on "Diffusion of innovations in service organizations: systematic review and recommendations".
ABSTRACT This article summarizes an extensive literature review addressing the question, How can we spread and sustain innovations in health service delivery and or- ganization? It considers both content (defining and measuring the diffusion of innovation in organizations) and process (reviewing the literature in a sys- tematic and reproducible way). This article discusses (1) a parsimonious and evidence-based model for considering the diffusion of innovations in health service organizations, (2) clear knowledge gaps where further research should be focused, and (3) a robust and transferable methodology for systematically re- viewing health service policy and management. Both the model and the method should be tested more widely in a range of contexts.
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ABSTRACT: This study aims to make a contribution to knowledge about how to implement evidence in residential aged care. The context for the study was the Encouraging Best Practice in Residential Aged Care Program, funded by the Australian Government to facilitate the implementation of evidence-based practice in residential aged care. The study drew on the experiences of those involved in the program to answer the question: what mechanisms influence the implementation of evidence-based practice in residential aged care and how do those mechanisms interact? The methodology used grounded theory from a critical realist perspective, informed by a conceptual framework that differentiates between the context, process and content of change. People were purposively sampled and invited to participate in semi-structured interviews. Fifty-one people were interviewed, in 44 interviews, between September 2009 and November 2010. With three exceptions, all interviews were conducted in person. Participants had direct experience of implementation in 87 facilities, across nine areas of practice, in diverse locations. Sampling continued until data saturation was reached. Literature was used to inform all stages of the study. The quality of the research was assessed using four criteria for judging trustworthiness: credibility, transferability, dependability and confirmability. Data analysis resulted in the identification of four mechanisms that accounted for the events that took place and the experiences of those events. The core category that provided the greatest understanding of the data was the mechanism On Common Ground. A series of factors – conversation, language, how care was framed, whether a proposed change ‘made sense’ and whether colleagues were alike or not alike in some way – were identified by participants as important elements of ‘common ground’; all of these factors served to place, or not place, individuals On Common Ground. Participants described learning as an essentially creative process, rather than simply the transmission of information from one person to another. At the core of creating knowledge was the mechanism Learning by Connecting, whereby people were able to connect new knowledge with existing practice and knowledge, think ‘outside the square’ to connect with additional knowledge and make connections between actions and outcomes. The process of integrating new practices took place in facilities that were highly structured in terms of routines, technologies, documentation systems, accreditation, funding and staffing. Relatively simple changes took place within complex structures, providing care to residents with complex needs. As the study progressed it became apparent that it was less about implementation of evidence-based practice than about the broader question of how to implement practice change. The fact that a change in practice was evidence-based had little influence on implementation. Participants described a situation where any new practice, whether evidence-based or not, had to compete with an existing set of constantly shifting priorities. Reconciling Competing Priorities was an ongoing mechanism whereby new practices either became part of routine care or did not. Even becoming part of routine care was no guarantee that a new practice would take place all the time – it always had to compete with other priorities. The mechanism Exercising Agency had close links with Reconciling Competing Priorities, bridging the gap between agency and action. It was the human dimension of change, both individually and collectively, that made things happen. Individuals may have possessed the necessary skills and knowledge to effect a change in practice, they may have learnt all there was to know about a proposed change, and they may have been able to reconcile the priorities facing them on a particular day, but they could still choose whether to act or not. The findings include many of the factors identified in the literature about how to change practices, but in a way that provides some explanatory power; this fits the definition of theory, albeit a tentative theory. Changes in practice did not result from a simple set of causal links. The various relationships between the four mechanisms were more subtle than that, and best described as ‘patterns of association’. This study’s various findings are consistent with the findings from other research, but the way they fit together is novel and adds to current knowledge about how to improve practices in residential aged care. The mechanisms open up many possibilities for further research, both within residential aged care and in health care more generally.01/2012, Degree: Doctor of Philosophy, Supervisor: Grace McCarthy, Alison Kitson
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ABSTRACT: It has been 10 years since No Child Left Behind (NCLB), with its emphasis on scientifically based instruction as a means for improving the educational outcomes of students, was signed into law. In this article, we review the basic tenets of evidence-based practice as they apply to education as well as the progress that has been made in identifying, implementing, and evaluating evidence-based interventions since NCLB. We conclude that the greatest threat to evidence-based education is poor implementation of interventions. If evidence-based interventions are not implemented well, then the expected benefits are not likely to be realized.Journal of Positive Behavior Interventions 10/2013; 15(4):214-220. · 1.69 Impact Factor
To the Editor:
Greenhalgh and colleagues produced a remarkable review of the diffu-
sion of innovations (Greenhalgh et al. 2004). We are also intrigued by
their new methodology, “meta-narrative review,” and look forward to
the publication describing their methodology that is currently in press.
We would like the authors’ opinion on whether complexity science
and the theory of complex adaptive systems (CAS) explain some of their
observations on the diffusion of innovation. Although they mentioned
complexity studies in their review, they did not mention CAS. The core
of their summary in Figure 3 is mechanistic process, although some of
the properties inventoried on the perimeter of the figure are attributes
of a CAS. For example, the authors described observations such as the
nonlinearity of assimilation as “organic” that other authors, including
Dr. Greenhalgh, have described as reflecting CAS (Plsek 2001; Plsek
and Greenhalgh 2001).
In the appendix, “Redesigning Health Care with Insights from the
Science of Complex Adaptive Systems,” of the Institute of Medicine’s
report Crossing the Quality Chasm: A New Health System for the 21st
Century, Plsek defines a CAS as “a collection of individual agents that
actions are interconnected such that one agent’s actions change the con-
text for other agents” (Plsek 2001, 312–3). In health care, agents may
be individuals or groups of individuals who contribute to the provision
Thus, the relationships and the history of agents are important. Com-
plexity science posits that in order to understand the organization, one
cannot look at individual parts in a mechanistic way. Rather, one must
study relationships and patterns within those relationships over time.
The state of a given system at a given time is a nonlinear function of the
state of that system at a previous time.
A critical tenet of complexity science is the nonlinear dependencies
among agents. Health services researchers often use linear models to
The Milbank Quarterly, Vol. 83, No. 1, 2005 (pp. 177–179)
c ?2005 Milbank Memorial Fund. Published by Blackwell Publishing.
attempt to explain phenomena. But what if the phenomenon we are
trying to explain does not fit this model? Complexity science argues
that when we try to model a nonlinear dynamic system with tradi-
tional statistical models, we can never begin to understand spontaneous,
self-organizing systems like those in health care (McDaniel and Driebe
2001). This may help explain why we find low r-squares in explanatory
models, why the speed of innovation varies in different organizations,
and why similar interventions in different settings may have different
Robert G. Badgett, M.D.
Mary Jo V. Pugh, Ph.D., R.N.
Veterans Evidence-based Research,
Dissemination, and Implementation
San Antonio, Texas
Greenhalgh, T., R. Glenn, F. Macfarlane, P. Bate, and O. Kyriakidou.
Review and Recommendations. Milbank Quarterly 82(4):581–629.
Care Management. Advances in Healthcare Management 2:11–36.
Plsek, P.E. 2001. Appendix B: Redesigning Health Care with Insights
of Medicine, 309–22. Washington, D.C.: National Academy Press.
Plsek, P.E., and T. Greenhalgh. 2001. Complexity Science: The Chal-
lenge of Complexity in Health Care. BMJ 323:625–8.
I agree with Drs. Badgett and Pugh that complexity science is an excel-
lent explanatory model for analyzing the spread of innovation in service
organizations, for precisely the reasons they have outlined. Table 1 of
our article lists “complexity studies” as one of 13 research traditions that
contributed to our systematic review. But the other 12 traditions pro-
vided additional insights that enriched our overall model. Depending
Mary Jo Pugh