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Implications of complexity theory for clinical practice and healthcare organization

R Tuffin BSc (Hons) MRCP FRCA EDIC FFICM PGCCE
DOI: http://dx.doi.org/10.1093/bjaed/mkw013 349-352 First published online: 26 April 2016
  • 1A01
  • 1A03
  • 2C01

Key points

  • Non-linear systems are not amenable to investigation by reductionist methods.

  • Complexity theory offers an alternative approach to quantifying the degree of physiological derangement in multi-system disorders such as sepsis.

  • The normal, healthy human heart rate displays fractal variation which is lost in numerous disease states.

  • Statistical techniques, such as approximate entropy, allow us to quantify the degree to which this variation is lost.

  • Large, multi-faceted organizations such as the NHS frequently behave as complex systems and as such may benefit from alternative management strategies, informed by complexity theory.

Medicine, like many other scientific fields, is founded upon the classical Cartesian method of reductionism, where a problem is broken down into its smallest components, examined, and then the information gleaned used to draw conclusions about the nature of the larger reality. Fundamental to this approach is the requirement that the problem being examined is a linear system (Table 1). When this is the case, the reductionist approach is a great success and the clinician may rightly feel confident in predicting the outcome of an intervention. An example of this might be the response of blood glucose to a dose of exogenous insulin.2 Frustrations arise however when the problem we wish to examine is not a simple linear system but rather shows non-linear behaviour (Table 1). Our inability to predict the outcome in these situations is all too painfully familiar, yet it was at this problematic interface, between reductionism and real life, that the science of complexity theory was born.

View this table:
Table 1

Features of linear and non-linear systems

Edward Lorenz was a meteorologist at the Massachusetts Institute of Technology who in 1961 was trying to develop a computer model to allow accurate long-range weather forecasting. While inputting the data to rerun a previous weather model, he abbreviated one number from 0.50612 to 0.506 …

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