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.
178 Download full-text
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