Healthcare teams as complex adaptive systems: Focus on interpersonal interaction
Introduction
A series of four papers in the BMJ in 2001 introduced the principles of complexity science in medicine and in healthcare [1], [2], [3], [4]. Authors conclude that ‘Clinical practice, organisation, information management, research, education, and professional development are interdependent and built around multiple self-adjusting and interacting systems’ [3]. The unpredictability and paradox that are present in each of the aforementioned topics, due to their self-adjusting and interacting properties, call for new conceptual frameworks with a dynamic and creative view of the world. These frameworks should replace the views of the traditional explanatory model in medicine based on scientific positivism that describes the cause-effect relationship between two isolated events, which is just one factor underlying the dynamics of the mentioned topics [5]. In this respect, complex situations, requiring adaptive and probing behaviour, are to be distinguished from complicated situations, requiring analytical cause/effect study and well-thought-through solutions. Complexity science belongs to the latest generation of systems thinking and studies complex systems [6], often called complex adaptive systems (CAS) by focusing on the relations and interconnections of the system components, rather than on the individual components themselves.
In the last few decades, complexity science has been cumulatively used as a theoretical framework in designing healthcare research to explore and understand complex healthcare-related issues [7]. A recent review on the use of complexity theory in health services research describes how authors identified relationships, self-organization, and diversity as the most frequently used attributes of complexity theory in health services research [7]. Consequently, every aspect and every level of healthcare has been framed and explored as complex adaptive systems: diseases, patients, practices, epidemiology, education, and organizations [1], [5], [8], [9], [10], [11], [12], [13], [14], [15].
Equally, healthcare teams have been described as CAS [9], [16], [17], [18]. The better understanding we acquire in this way, the better we may be able to optimize healthcare delivery. Research into the dynamics of healthcare teams using complexity science principles has, so far, mainly been explorative and descriptive [7]. Using a tool to objectify and quantify team functioning and interprofessional relationships could be of added value in understanding and optimizing team functioning. Table 1 shows the core principles of CAS, each illustrated by an example of healthcare team functioning.
Interprofessional healthcare teams might operate in complex situations with high patient care needs and rapidly changing societal contexts. In order to deliver high quality patient care, healthcare teams need to adapt efficiently to the changing environment.
The adaptability of a team is subject to the communication and interactions between team members, as the team members’ behaviour is based on their past interactions, and current and past interactions together pave the way for future behaviour [19], [20], [21]. Those interactions, resulting in team behaviour that addresses care needs in complex and uncertain circumstances, can be described with the use of the certainty-agreement diagram (see Fig. 1) [22].
The diagram has two axes. On the X-axe, the degree of certainty is displayed: will a certain action lead to a specific outcome? The Y-axe represents the level of agreement team members have on the relationship between action and outcome. The bottom left-hand corner of the diagram is the zone with maximum certainty and agreement: a high certainty of a causal relationship between action and outcome with all team members agreeing on it. The most efficient way for a team to work on tasks situated in this zone is a straightforward hierarchical model: orders are being given and being executed. An example of such a situation is a patient with a cardiac arrest where team members use protocols to deliver standardized care. The top right-hand corner is the zone of chaos, where there is hardly any relationship between actions and outcomes and team members have no agreement. An example of such a situation might be an outburst of an unknown infectious disease to be handled by a team which is neither equipped nor prepared for this. Many situations in healthcare, however, are situated in the middle area of the diagram, the complexity zone. In this zone, agreement and certainty are insufficient to predict the best way to plan a series of actions towards a certain goal (as in simple linear systems), although team members generally agree on the result of a single specific token action. As a result, creativity, experimentation, trying out different approaches is the most efficient strategy to handle these situations [3]. As will be described in the Methods section (2.2 Participants), palliative home care teams are working in this complexity zone.
The strategy to function in the complexity zone requires a continuous interaction and deliberation between team members, as well as a continuous monitoring of the effect of their actions and adjusting the next step accordingly [23].
The aim of this study is to test the feasibility of a tool to objectify the functioning of healthcare teams operating in complexity zone, and to evaluate its usefulness in identifying areas for team quality improvement.
Section snippets
CAL™ organisational capability questionnaire [24]
To evaluate the functioning of healthcare teams in the zone of complexity, we use a team measurement instrument based upon the complexity science framework. The Complex Adaptive Leadership (CAL™) Organisational Capability Questionnaire (OCQ) developed by Obolensky has been developed and extensively tested in corporate teams and companies [27]. The score on the CAL™ OCQ (to be filled out by team members) provides us with a general indication on a team’s capability to operate as a self-organizing
Group 1
All members of the largest palliative-care team who were present during the team meeting (n = 15) completed the questionnaire: two coordinators, two physicians, three secretaries and eight nurses. The two team psychologists were not present during the meeting and did not participate. All participants gave oral informed consent after the purpose of the questionnaire was explained.
Group 2
All 15 physicians attending the educational meeting agreed to participate. These physicians represented 13 teams out of
General results
This is, to our knowledge, the first study that presents a quantitative evaluation of the day-to-day functioning of healthcare teams and professionals according to the complexity science principles. While descriptive accounts of healthcare teams as complex adaptive systems have been published, providing us with insights into the team dynamics [9], [18], [25], quantifying the level of adaptability of healthcare teams as we did in our study may allow for benchmarking the team’s functioning as a
Conflict of interest
None.
Funding
No funding was received for this study.
Acknowledgements
We are grateful to Nick Obolensky for granting us permission to use the CAL™ OCQ in this study and for discussing with us the results of the analysis. We are grateful to Kristien Temperville for proofreading the manuscript.
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