Complexity Science and Palliative Care: Drawing from Complex Adaptive Systems Theories to Guide Our Successes

Abstract

Dear Editor:

Many palliative care problems are complex; they may be difficult to understand, describe, predict, and manage, and thus not amenable to simple solutions. Palliative care scholars have argued that the principles of complexity science are particularly well suited to palliative care because clinical problems such as suffering, quality of life, and goals of care are inherently complex.^1,2 Complexity science is the study of complex adaptive systems (CAS), and applying the principles of complexity science can help identify approaches to complex problems.³ Here, we advance this argument and propose that to succeed in creating sustainable and integrated systems of palliative care, those involved in palliative care play a critical role in helping to understand and then apply the principles of complexity science.

Scenario 1: Mr. G, a 63-year-old widowed man with colon cancer and metastases to his liver, is being followed for constant, achy, generalized abdominal pain. At rest this is well controlled with long-acting hydromorphone. Incident abdomen pain occurring with movement has been effectively managed with breakthrough hydromorphone before activity. Mobilizing has become increasingly difficult and has led to Mr. G feeling anxious about losing his independence. He has not revised his will since his wife's death, and has an upcoming meeting with his lawyer. Mr. G now states that he is experiencing severe episodes of abdominal pain at rest. These do not respond well to his short-acting hydromorphone.

Scenario 2: Mrs. W, a 71-year-old with advanced chronic kidney disease, is admitted to hospital with intermittent confusion and increased uremia. Mrs. W's most responsible team is concerned about her decision-making capacity and ask the palliative care team to address her goals of care. Six months ago, Mrs. W. was offered dialysis but declined at that time, opting instead for more conservative management. Her son is her substitute decision maker and he has not been present for past discussions regarding the management of Mrs. W's chronic kidney disease. He has now indicated that if his mother does not have decision-making capacity, he will want all “reasonable” life-sustaining medical treatments, short of CPR, including dialysis.

The two palliative care scenarios are common examples of a CAS. Greater than the sum of its parts, a CAS's individual components are not fully knowable, may interact unpredictably, can produce unintended outcomes, and are best approached as entangled and dynamically interacting.³ Innovation results when, in the midst of seemingly chaotic activity, actions are guided by a few simple steps or “minimum specifications.”³ With the lens of complexity science, it is understood that the system and external environments are not constant. The system comprises uncertainty and paradox, and the individuals function as independent creative decision makers.³ In each situation, the provider is required to use intuition and imagination.¹

There is increasing uptake in the application of complexity science in such fields as biology, physics, economics, and, more recently, healthcare especially in areas of systems planning, healthcare leadership development, organizational behavior, and clinical service delivery.³ Practical guidance on applying complexity theory to a healthcare problem begins with the critical first step of clarifying problem type (Table 1). For a simple or a “complicated” problem, one expects a certain outcome to be achieved by replicating solutions that are either known or knowable.¹ In contrast, a list of steps or guidelines toward a solution cannot be generated for a complex problem.¹ A complex problem is inherently unique and can only be understood in individual terms. Although experience adds value, it does not provide the necessary conditions to assure “success.” Simple palliative care problems (e.g., writing an opioid prescription) and complicated palliative care problems (e.g., pathophysiology of neuropathic pain) represent only one part of the skill set required to provide excellent palliative care. Much of what we struggle with, in alleviation of suffering, is indeed “CAS complex.”

Table 1.

Problem Types within Palliative Care Systems

	Simple	Complicated	Complex
Example	Prescribe an opioid	Pathophysiology of neuropathic pain	Goals-of-care discussion
Solution	Known	Knowable	Unknowable
Preparation	Plan details	Plan details with precision	Good enough sense of direction
Process	Technically correct steps are replicated and consistently produce the same outcome	Greater expertise is required to address steps that are technically more challenging	Replicating a successful process will NOT reliably reproduce an outcome
Outcome	Intended and set as the aim	Intended and set as the aim	Highly variable; emerges in time

Experienced palliative care clinicians know that the approach to the problem of Mr. G's pain in Scenario One requires addressing all interconnected elements of his story. There is an awareness of the individual components of the patient's lived pain experience, which are then explored and attended to, to allow new ways to address the pain to emerge. This approach to “total pain” is widely understood to be highly individualized.

Similarly, in Scenario Two, Mrs. W's situation is likely best approached using a divergent approach as the goals-of-care discussion begins by seeking to understand perspectives rather than targeting a specific outcome. Minimizing the existential and overemphasizing the instrumental components of conversation may result in a checklist of steps to ascertain patient preferences.⁴ Such checklist-driven approaches are useful only once the complex problem has been understood, and this awareness and approach are often overlooked.¹ As complexity theory would suggest, both scenarios outline problems that can be moved forward even if they cannot be immediately “solved.”¹ Solutions are usefully approached as emergent properties that result from the interactions among all agents in the system, including the patient.³

Scenarios such as these highlight the complex nature of many clinical palliative care problems as well as the skills palliative care providers may already have in applying complexity theory. We postulate that excellence in palliative care requires clinicians, healthcare systems, organizations, and planners to develop skills in applying the principles of complexity science and approach the provision of palliative care as a complex problem. Complexity science suggests that “readiness to change” occurs when a system is in a state far from equilibrium; there is then a sufficient tension to change.¹ In such circumstances, a small influence can have a large effect on behavior and effective solutions may be realized. Palliative and end-of-life care appears to be at important self-reflective crossroads in its own evolution; its place in society and systems suggests such readiness is at hand.

Are we aware of the many dynamic situations we are in as clinicians and systems planners? If so, to what examples can we turn where a CAS approach has been key to success or to planning? As end-of-life care, and the very nature of palliative care, pauses to examine its successes and failures, perhaps the time for CAS thinking and application to palliative care is now.

References

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