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Delivering Health Care in Complex Adaptive Systems III: The Diverse Challenges

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As pointed out in the Meikirchian model of health—the relationship between agents within a complex adaptive system outweighs the importance of the status of each individual, including, in this case, the patient.  The authors also point out that we miss a great deal of complexity by only focusing on the number of chronic conditions a patient has—on the status of the patient.    Complexity, as a dynamic system, draws our attention to previously missed ways of seeing the fluid and often turbulent nature of health and healthcare complexity. Stepping back to look through a complexity lens allows us to see more clearly what is actually happening in our healthcare interactions and how best to address previously missed factors that contribute substantially to patient outcomes.

Complexity of Cancer Screening

In their compelling review of complexity interventions in healthcare setting, Braithwaite and associates (Breathwaite 2021) provide a window into successful interventions using complexity science and approaches in healthcare.  Each narrative is compelling. However, we will share just one to provide a picture of how the complexity approach to healthcare quality and safety may improve our capacity to influence healthcare more effectively while developing new leadership skills in the process. Braithwaite and his colleague begin by exploring how a community of clinicians responded to receiving a high-risk result for a cancer screening test. They describe the difference between “care as imagined” vs “care as done.” They begin with the linear, mechanistic “imagined care” model most of the clinicians believed was happening:

  1. Screening tests carried out to assess risk of cancer being hereditary.
  2. Results reported as a high or low risk.
  3. People with high-risk result were referred to a genetic service.

The study included multiple clinician stakeholders—surgeons, medical and radiation oncologists, pathologists, and genetic specialists. The research team developed a process map of “referral as done”, which revealed several important factors that greatly reduced & delayed the referral rates—such as fear of overwhelming the patient with a new issue, peri-operative complications that delayed discussion of test results with the patient and family, lack of consensus on who was going to make the referral, and lack of clarity on how to document the results in the electronic medical record.

None of these factors were included in the “referral as imagined” linear process developed at the beginning of the study—yet most real-time quality and performance improvement programs in healthcare are based on the “process as imagined” more than the “process as done.” The introduction of frontline Lean efforts such as Kaizen are the rare exception—by developing process maps and real-world workflows and engaging frontline in the process.  Yet, many of these Kaizen-type efforts have challenges being integrated into the larger healthcare system—often acting more as “idealized retreats” than long-term impactful change processes, in large part due to enormous clinical inertia and leadership inertia that often resist even the best laid out plans for improvements.

So, how did Breathwaite and associates impact the gap between “as imagined” and “as done”?  They brought together all the agents (in this case they facilitated meetings of all the specialists together) to clarify roles, review the troubling referral rate completion data, examine barriers and helped create a shared mental model that fostered a whole-system approach to the care of the patients.  Deepening the understanding and relationship between the physician agents allowed for meaningful shifts in referral rates that allowed them to achieve results that better matched their expectations while recognizing the many barriers that must be overcome to get there. They did not blame one specialty or another, or scapegoat nurses or care managers—they focused on relationships and increased teamwork across the several specialties to navigate the complexity inherent in what seemed like a very simple, linear process “as imagined” yet was a mess of barriers and dropped balls “as done.”

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