Complexity science

Healthcare is increasingly recognised as a complex adaptive system, comprised of multiple levels of interacting agents, including diverse organisations (e.g., hospitals, not-for-profits, governments, professional bodies), groups (e.g., medical teams, online patient communities) and individuals (e.g., doctor, nurse, patient, carer, pharmacist). All of this interconnection leads to nonlinearity, making understanding, influencing and improving the healthcare system a challenge of the greatest magnitude. For example, complexity science highlights the importance of local context, such that an intervention that works in one hospital might show little improvement or might not even be adopted in another. Furthermore, feedback loops within complex systems mean that changes may not be sustained, as their effects become self-correcting (negative feedback); alternatively, a small perturbation in one part of system, for example a doctor failing to wash their hands, may have repercussion across the entire network, such as causing frustration among their team, patient infection, culminating in a national scandal.

While such complexity belies easy prediction, our research focuses on ways to understand this complex system, with the goal of improving healthcare. Led by Professor Jeffrey Braithwaite, we look at the links and disconnects of agents in a complex system through social network analysis which can highlight common phenomena such as silos. We also find ways to make sense of this complexity through qualitative methods, such as observation, ethnography, and quantitative analysis, in particular utilising “Big Data”. In addition, we are interested in understanding and making use of feedback for the purposes of healthcare improvement. 

Read more in our White Paper, entitled Complexity Science in Healthcare – Aspirations, Approaches, Applications and Accomplishments.

 

Stream Team Members