Complexity science offers an alternative view to inform research. The theory emerged in the 1950’s from the Santa Fe Institute where anthropologists studying civilisations noticed that many successful ones came to an abrupt end rather than experiencing a slow decline, suggesting a single catastrophic event may have been responsible (Lewin, 1999). Theorists were interested in the complexity of such cultures, similarities of events leading to their end and the concept that civilisations were more complex than linear. Further work involving computing science
developed theories based upon the concept of the world being chaotic but within the chaos elements emerge through adapting (Lewin, 1999). Numerous disciplines have since adopted complexity theory including meteorology, physics, mathematics and genetics (McMillan, 2008; Mitchell, 2009). Theories of complexity challenge the nature of cause and effect thinking.
Central to complexity theory is the concept of a system which has been defined as a
“delineated part of the universe which is distinguished from the rest by an imaginary boundary“ (Martin & Félix-Bortolotti, 2010, p 417). Whilst Aristotle considered that
the whole is greater than the sum of the parts (Martin & Sturmberg, 2009), systems thinking sees the whole as interconnected, interacting and interdependent parts (De Simone, 2006). The focus on the whole is considered more meaningful through the study of complexity as it focuses on determining how the whole exists through
67 how the parts are organised whereas reductionism focuses on single cause and effect or interventions (De Simone, 2006). Through understanding the complex relationships and patterns of the whole it is therefore possible to understand the system (Anderson et al., 2005).
As noted in the previous section, this is not an argument that EBM should be abandoned. Indeed, it should be noted that complexity science does not dismiss evidence based approaches (De Simone, 2006) and it is acknowledged that such reductivism may be useful for the mechanical aspects of medicine (Sturmberg & Martin, 2009). However, for complex technical systems such as health care to be effective there is a need to understand heterogeneity (Martin & Félix-Bortolotti, 2010).
An awareness of complexity may not solve a problem but opens it up to a greater awareness through improving the understanding of complex relationships within the system as opposed to merely considering the relationship of discreet elements (Martin & Sturmberg, 2009). Therefore, approaching research from the perspective of complexity science allows for finding out how an organisation learns rather than what it knows (Anderson et al., 2005).
There are several features present in complex systems. They are made up of individual components and networks that exhibit collective behaviour, information is processed from both the internal and external environment and they are able to adapt (Mitchell, 2009). That is, the behaviour within the system is hard to predict but can change through learning or evolution in order to improve the possibility of survival or success (Mitchell, 2009).
Complex systems are also autonomous in that what they become is part of the system where the components within the surrounding environment may impact (Byrne, 2011). These multiplied relationships result in the potential for new states through adaption, that is, the system is open (Byrne, 2011; Dekker, 2011).
68 Systems may be described as simple or complex (Byrne, 2011). A simple system exists where a single cause can be determined and a causal category can be segregated from another. By comparison a complex system has causal categories that are intertwined and cannot be completely described in such a dualistic manner. One system may intersect with another or be nested within it and the boundaries may be fuzzy.
These concepts can be applied to complex organisations and they assist in
explaining the nature of serious organisational failure. Systems can become resilient and are also able to adapt, which means they may not ever be fully describable (Dekker, 2011). Rather than describing the ‘whole’ as the sum of its parts, it is considered that the ‘whole’ has emergent parts (Dekker, 2011; McMillan, 2008). This emergence cannot be understood by analysis alone (Byrne, 2011).
Complexity has been described as a subset of chaos. It is from chaos that
emergence comes which may include “something that you couldn’t have predicted from what you know of the component parts” (Langton cited in Lewin, 1999, pp. 12—13). The emergent global structure influences behaviour at the local level which then feeds back to the global structure in a way that produces further influences.
It has been observed that in the traditional model which is based upon Newtonian science, something must break in order to indicate failure (Dekker, 2011). This reliance upon a Newtonian view of the world is problematic as it fails to recognise complexity. Complex systems are dynamic and non-linear, and the system may be robust enough to sustain the change (Byrne, 2011) or a small change may have a significant impact (Dekker, 2011; McMillan, 2008).
The notion of small changes having an impact is often referred to as the butterfly effect, a term that has its origins in a model for meteorology that considered whether the wing flap of a butterfly could cause a tornado. The conclusion was the single event of a butterfly flapping its wings could lead to a tornado depending upon
69 what other elements were present in the weather system at the time (Lorenz, 1972).
Behaviour within an organisation emerges from the bottom up rather than top down and through adaption results in systems that develop a capacity to function at maximum capability. This is termed the edge of chaos (Dekker, 2011; McMillan, 2008). That is, the organisation (system) may appear to be chaotic but those working within it have adapted to form complex ways of operating. Hence, when the equivalent of a butterfly flapping its wings occurs there may be no effect, or the outcome may be extremely dramatic.
Once this point is reached there is a risk that a phase shift may result (Dekker, 2011). Other terms such as a phase transit or tipping point have also been used to describe this concept which refers to a little bit more or less of something leading to something very different. An example of such a process from the physical sciences is the impact temperature on an object’s solid, liquid or gaseous form (Lewin, 1999). For example, although possible temperatures have a large range, H2O changes its
form from solid to liquid and from liquid to gas at a specific temperature. A cup of water remains liquid even with a shift in temperature from 20—90 degrees but a small change from 95—100 degrees results in a significant change of form. When a phase shift or tipping point occurs within an organisation operating at the edge of chaos it is possible for a major system ( or organisational) failure or disaster to occur (Dekker, 2011).
This view of how organisations fail has similarities with the Swiss Cheese Model
referred to in the previous chapter (Reason, 1997). A latent error, for example, may be the eventual tipping point for a major incident. However, the nature of such a model is linear in that it may explain the events leading up to an organisational failure but the result of this type of thinking is to react to error and the constant measurement of the absence of safety rather than its presence (Hollnagel, 2014). It
70 is possible that the focus on measurement of error is an unintended consequence of a focus on cause and effect approaches to error management and research. It has been noted in the previous chapter that the concept of resilience engineering
may offer an alternative approach to error management in complex-technical systems (Qureshi, 2007). Complexity, resilience and an acknowledgment of adaptive systems are underlying elements of this concept.
Focusing on the measurement of error, (the non-presence of safety) is referred to as Safety I. The alternative approach is Safety II where the focus is on what has gone right. When things in an organisation continually go right the organisation is
considered resilient. Even when things do go wrong (that is, an error occurs) an organisation may be able to deal with the error and adapt in such a way that it continues to exist. This is further indication of resilience. Therefore, in order to understand how safety exists, there is a need to understand how organisations are resilient.
Organisations or systems that are continually resilient and that achieve greater levels of successful performance have been referred to as positive deviants (Lawton, Taylor, Clay-Williams, & Braithwaite, 2014). It has been proposed that a better understanding of safety may be achieved through identifying these, studying them in-depth to find the processes and practices that allow them to succeed (using qualitative methods), developing and testing findings in larger statistical studies followed by working with key stakeholders to “disseminate the evidence about newly characterized best practices” (Lawton et al., 2014 p. 881).
The focus of such an approach still hinges on the concept of evidence and large statistical studies that focus on intervention and outcome rather than complexity. The authors note that measuring concepts such as safe care is difficult as there is often variation found in measures amongst different work settings and professions (Lawton et al., 2014). Such differences in relation to medication error and safety climate have already been outlined in the previous chapter.
71 The challenge is how to better understand the complex health system, the sub- systems within it and how best to undertake research that is able to inform practice. Despite arguments that paradigms of knowledge generation need to reflect reality (Martin & Félix-Bortolotti, 2010) there is resistance to such change (De Simone, 2006). These types of statements assume an understanding of what constitutes knowledge. This will be explored further in the next section in relation to how knowledge is generated in a health care system that is complex.