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"Complexity science"
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Allometric Scaling of Mutual Information in Complex Networks: A Conceptual Framework and Empirical Approach
Complexity and information theory are two very valuable but distinct fields of research, yet sharing the same roots. Here, we develop a complexity framework inspired by the allometric scaling laws of living biological systems in order to evaluate the structural features of networks. This is done by aligning the fundamental building blocks of information theory (entropy and mutual information) with the core concepts in network science such as the preferential attachment and degree correlations. In doing so, we are able to articulate the meaning and significance of mutual information as a comparative analysis tool for network activity. When adapting and applying the framework to the specific context of the business ecosystem of Japanese firms, we are able to highlight the key structural differences and efficiency levels of the economic activities within each prefecture in Japan. Moreover, we propose a method to quantify the distance of an economic system to its efficient free market configuration by distinguishing and quantifying two particular types of mutual information, total and structural.
Journal Article
When complexity science meets implementation science: a theoretical and empirical analysis of systems change
Background
Implementation science has a core aim – to get evidence into practice. Early in the evidence-based medicine movement, this task was construed in linear terms, wherein the knowledge pipeline moved from evidence created in the laboratory through to clinical trials and, finally, via new tests, drugs, equipment, or procedures, into clinical practice. We now know that this straight-line thinking was naïve at best, and little more than an idealization, with multiple fractures appearing in the pipeline.
Discussion
The knowledge pipeline derives from a mechanistic and linear approach to science, which, while delivering huge advances in medicine over the last two centuries, is limited in its application to complex social systems such as healthcare. Instead, complexity science, a theoretical approach to understanding interconnections among agents and how they give rise to emergent, dynamic, systems-level behaviors, represents an increasingly useful conceptual framework for change. Herein, we discuss what implementation science can learn from complexity science, and tease out some of the properties of healthcare systems that enable or constrain the goals we have for better, more effective, more evidence-based care. Two Australian examples, one largely top-down, predicated on applying new standards across the country, and the other largely bottom-up, adopting medical emergency teams in over 200 hospitals, provide empirical support for a complexity-informed approach to implementation. The key lessons are that change can be stimulated in many ways, but a triggering mechanism is needed, such as legislation or widespread stakeholder agreement; that feedback loops are crucial to continue change momentum; that extended sweeps of time are involved, typically much longer than believed at the outset; and that taking a systems-informed, complexity approach, having regard for existing networks and socio-technical characteristics, is beneficial.
Conclusion
Construing healthcare as a complex adaptive system implies that getting evidence into routine practice through a step-by-step model is not feasible. Complexity science forces us to consider the dynamic properties of systems and the varying characteristics that are deeply enmeshed in social practices, whilst indicating that multiple forces, variables, and influences must be factored into any change process, and that unpredictability and uncertainty are normal properties of multi-part, intricate systems.
Journal Article
Notes on complexity : a scientific theory of connection, consciousness, and being
Nothing in the universe is more complex than life. Throughout the skies, in oceans, and across lands, life is endlessly on the move. In its myriad forms - from cells to human beings, social structures, and ecosystems - life is open-ended, evolving, unpredictable, yet adaptive and self-sustaining. Neil Theise's book is an introduction to complexity theory, the science of how complex systems behave - from cells to human beings, ecosystems, the known universe and beyond - that profoundly reframes our understanding and illuminates our interconnectedness.
BOOK
Saving Human Lives: What Complexity Science and Information Systems can Contribute
We discuss models and data of crowd disasters, crime, terrorism, war and disease spreading to show that conventional recipes, such as deterrence strategies, are often not effective and sufficient to contain them. Many common approaches do not provide a good picture of the actual system behavior, because they neglect feedback loops, instabilities and cascade effects. The complex and often counter-intuitive behavior of social systems and their macro-level collective dynamics can be better understood by means of complexity science. We highlight that a suitable system design and management can help to stop undesirable cascade effects and to enable favorable kinds of self-organization in the system. In such a way, complexity science can help to save human lives.
Journal Article
The three numbers you need to know about healthcare: the 60-30-10 Challenge
Background
Healthcare represents a paradox. While change is everywhere, performance has flatlined: 60% of care on average is in line with evidence- or consensus-based guidelines, 30% is some form of waste or of low value, and 10% is harm. The 60-30-10 Challenge has persisted for three decades.
Main body
Current top-down or chain-logic strategies to address this problem, based essentially on linear models of change and relying on policies, hierarchies, and standardisation, have proven insufficient. Instead, we need to marry ideas drawn from complexity science and continuous improvement with proposals for creating a deep learning health system. This dynamic learning model has the potential to assemble relevant information including patients’ histories, and clinical, patient, laboratory, and cost data for improved decision-making in real time, or close to real time. If we get it right, the learning health system will contribute to care being more evidence-based and less wasteful and harmful. It will need a purpose-designed digital backbone and infrastructure, apply artificial intelligence to support diagnosis and treatment options, harness genomic and other new data types, and create informed discussions of options between patients, families, and clinicians. While there will be many variants of the model, learning health systems will need to spread, and be encouraged to do so, principally through diffusion of innovation models and local adaptations.
Conclusion
Deep learning systems can enable us to better exploit expanding health datasets including traditional and newer forms of big and smaller-scale data, e.g. genomics and cost information, and incorporate patient preferences into decision-making. As we envisage it, a deep learning system will support healthcare’s desire to continually improve, and make gains on the 60-30-10 dimensions. All modern health systems are awash with data, but it is only recently that we have been able to bring this together, operationalised, and turned into useful information by which to make more intelligent, timely decisions than in the past.
Journal Article
Healthcare teams as complex adaptive systems: understanding team behaviour through team members’ perception of interpersonal interaction
Background
Complexity science has been introduced in healthcare as a theoretical framework to better understand complex situations. Interdisciplinary healthcare teams can be viewed as Complex Adaptive Systems (CAS) by focusing more on the team members’ interaction with each other than on the characteristics of individual team members. Viewing teams in this way can provide us with insights into the origins of team behaviour. The aim of this study is to describe the functioning of a healthcare team as it originates from the members’ interactions using the CAS principles as a framework and to explore factors influencing workplace learning as emergent behaviour.
Methods
An interview study was done with 21 palliative home-care nurses, 20 community nurses and 18 general practitioners in Flanders, Belgium. A two-step analysis consisted of a deductive approach, which uses the CAS principles as coding framework for interview transcripts, followed by an inductive approach, which identifies patterns in the codes for each CAS principle.
Results
All CAS principles were identified in the interview transcripts of the three groups. The most prevalent principles in our study were principles with a structuring effect on team functioning: team members act autonomously guided by internalized basic rules; attractors shape the team functioning; a team has a history and is sensitive to initial conditions; and a team is an open system, interacting with its environment. The other principles, focusing on the result of the structuring principles, were present in the data, albeit to a lesser extent: team members’ interactions are non-linear; interactions between team members can produce unpredictable behaviour; and interactions between team members can generate new behaviour. Patterns, reflecting team behaviour, were recognized in the coding of each CAS principle. Patterns of team behaviour, identified in this way, were linked to interprofessional competencies of the Interprofessional Collaboration Collaborative. Factors influencing workplace learning were identified.
Conclusions
This study provides us with insights into the origin of team functioning by explaining how patterns of interactions between team members define team behaviour. Viewing healthcare teams as Complex Adaptive Systems may offer explanations of different aspects of team behaviour with implications for education, practice and research.
Journal Article