What is a COMPLEX ADAPTIVE SYSTEM?

A complex adaptive system is an open, dynamic and flexible network that is considered complex due to its composition of numerous interconnected, semi-autonomous competing and collaborating members. This system is capable of learning from its prior experiences and is flexible to change in the connecting pattern of its members in order to fit better with its environment. 

Complex adaptive systems are characterized by emergent behaviors as opposed to predetermined ones and self-organized controls instead of hierarchical or central controls. The weather, the stock market, and the health care system are examples of complex adaptive systems.  The healthcare system is composed of autonomous individuals that are working hard on providing care for their patients. These individuals interact constantly in a nonlinear way and are continually faced with external and internal changes such as patient’s medical status, insurance requirements, regulations, new research findings, members’ turnover and legal issues.

Traditional organizational theory leads us to view healthcare systems as machine-like with replaceable parts and behaviors that could be predicted and replicable based on past performance data. This view assumes that stability is the natural state of these systems; that systems consist of functions and roles that are carried out by replaceable employees; and that financial incentives, regulatory policies, detailed inflexible protocols, and best practice initiatives are the only recipes for introducing a change to these systems. 

Prior research shows that such standardized protocols are typically not feasible, replicable, or effective due to the unique local interactions of patients, employees, resources, and other demands faced by these complex adaptive systems. Recognizing the properties of complex adaptive systems, it becomes apparent why many prior efforts in building research processes within these systems have been unsuccessful and why scientific evidence alone does not necessarily predict adoption of new innovations. 

REFLECTIVE ADAPTIVE PROCESS

While this new understanding may be intuitively attractive, a strategy is needed that incorporates complexity.  There are many published strategies to help manage organizational change.  One such strategy is the reflective adaptive process developed by the NIH.  Reflective adaptive process is a practical method for using complex adaptive system principles to introduce acceptable, locally matched, and effective change in a health care system. Reflective adaptive process facilitates the development of local strategies, not prescribed protocols, for changes that are built on explicit opportunities for learning, reflection, and adaptation. There are five guiding principles to the reflective adaptive process:

1. Vision, mission, and shared values are fundamental in guiding ongoing change processes in a complex adaptive system
2. Creating time and space for learning and reflection is necessary for complex adaptive system to adapt to and plan change
3. Tension and discomfort are essential and normal during complex adaptive systems change
4. Improvement teams should include a variety of system’s agents with different perspectives of the system and its environment
5. System change requires supportive leadership that is actively involved in the change process, ensuring full participation from all members and protecting time for reflection.

A cross-functional team, meeting regularly, uses iterative cycles to identify priorities, discuss potential solutions, pilot small changes, and reflect on the impact of changes.  An internal or external facilitator guides the meetings by gathering information, stimulating self-reflection, and encouraging action.

References

McDaniel RR, Jr., Jordan ME, Fleeman BF. Surprise, Surprise, Surprise! A complexity science view of the unexpected. Health Care Manage Rev 2003;28(3):266-78.

Stroebel CK, McDaniel RR, Jr., Crabtree BF, Miller WL, Nutting PA, Stange KC. How complexity science can inform a reflective process for improvement in primary care practices. Jt Comm J Qual Patient Saf 2005;31(8):438-46.

Kochevar LK, Yano EM. Understanding health care organization needs and context. Beyond performance gaps. J Gen Intern Med 2006;21 Suppl 2:S25-9.

Rubenstein LV, Pugh J. Strategies for promoting organizational and practice change by advancing implementation research. J Gen Intern Med 2006;21 Suppl 2:S58-64.

Grimshaw J, Eccles M, Thomas R, et al. Toward evidence-based quality improvement. Evidence (and its limitations) of the effectiveness of guideline dissemination and implementation strategies 1966-1998. J Gen Intern Med 2006;21 Suppl 2:S14-20.

Inouye SK, Bogardus ST, Jr., Charpentier PA et al. A multicomponent intervention to prevent delirium in hospitalized older patients. N Engl J Med 1999;340:669-76.

Boustani M, Munger S, Beck R, Campbell N, Weiner M. A Gero-Informatics Tool to Enhance the Care of Hospitalized Older Adults with Cognitive Impairment. Clinical Interventions in Aging (in press).

Boustani M, Munger S, Weiner M, Beck R, Schubert C, Campbell N, Callahan C. Developing A CDSS To Care For Hospitalized Elders with CI: The e-CHAMP Study. The Proceedings of the 10th International Conference on Alzheimer's Disease and Related Disorders 2006 (in press).

Back to top