Table 4 The attributes of complexity theory underpinning the methodological framework used and the interpretation of the results observed

Agents

Individual agents work within a complex system

Interactions

The agents interact exchanging information or directing the flow that the information can go

This case-study focused on the agents and the main components of the health system that they act within. Involving the main agents in the co-creation approach facilitated the construction of a map that illustrates the relationship between the different components across the health system, identifies the elements of care within, and the interactions between these components (Tier 1 and 2).

Interactions in tier 1 were displayed unidirectionally. This helped identify the navigational path between components of the health system (e.g. the patient or caregiver must interact with certain components to progress through the system). In this case-study we did not define the criteria to initiate interaction or the specific nature of the interaction but acknowledge the need to do so to facilitate future state planning.

Non-linearity

These interactions are not all equal and the effect of one interaction may have a larger effect

Complexity theory acknowledges that complex systems are non-linear making the impact of interactions unpredictable. Non-linearity demands that important system outcomes be measured in the future state system performance assessment.

Unpredictability

Individual agents have freedom to work in ways which are not predictable, therefore it is hard to predict the effect of an intervention

The agents defined across sectors in our health ecosystem act autonomously. Program volumes, the extent to which care elements are implemented, program standards and outcomes are not known across the whole health system. It is therefore hard to predict the effect of interventions across the system. Unpredictability supports performance and outcomes measurement at the agent level.

Path dependence

Complex systems display sensitivity to their initial conditions and therefore two comparable systems may behave differently depending on their histories

The health care ecosystem mapping process revealed that resource availability was not uniform across the system and system components were limited by resource availability. For example primary care providers had different resource availability based on family health team and geographical location. Therefore path dependence will be important to consider in the redesign of the system; consideration of how components operated in the past may differentially influence intervention impact across the system.

Self-organization

An internal characteristic of the system to adapt intrinsically to increase system stability

Self-organisation was elegantly described by primary care providers. Resource availability limited the ability to offer extended primary health care to address the psychosocial health needs for their patients. Therefore, the system self-organised with acute care dominating interventions. This structure enhances stability by ensuring unstable patients are prioritized. Further work should be done to identify leverage points that might foster more proactive and preventive care as well as consider how the current rules governing resource distribution contribute to present inequities and inefficiencies.

Emergence

The individual interactions between components of a system may produce a property of the system that is different than the sum of the overall individual agent behaviours

The health care ecosystem map started with a “zoomed out” version of the system. The purpose was to be able to consider the emergent system as a whole rather than narrowing the focus, viewing only the parts or components perceived to be key to the target diseases. The aim was to encourage an understanding of the system’s functional behaviour as a web of interactions.

Open system

Undefined boundaries, the boundaries are permeable

Diversity

Composed of differing elements

The health system is an open system, that can re-organise considerably as a result of external stimuli. Not only is the outcome of any intervention unpredictable but the response of the system to any outside disturbance is unknowable. In this case-study we applied disease specific constraints as a boundary setting, designed to balance the complexity associated with boundary permeability with the limitations of a simplified but workable model.

We acknowledge that patients are diverse in terms of their health care needs and experience of the health care system. However, underpinning diversity and one of the drivers of these differentials are broader social, economic and contextual factors. One limitation of the current system is treatment of individuals as units of disease and not necessarily as people who themselves exist within a complex ecosystem of unequally distributed resources too. This study revealed system boundaries are permeable. It could be valuable to visualise a system where the components are more sensitive and reactive to the complexity of the biopsychosocial existence and try to identify leverage points for intervention to promote this version.