Analysing “the Workings” of Health Systems as Complex Adaptive Systems

  • Joachim P. Sturmberg


Complex adaptive systems can be analysed in two very different ways.
  • Looking backwards asking: which of its structures and behaviours allowed its current state to emerge (the what and why questions)

  • Looking forward asking: changes to which of its structures and behaviours may most likely shape dynamics that achieve a future desired outcome (the how questions)

Looking backwards decomposes the system. Decomposition of systems can produce:
  • Health atlases that describe the distribution of various health services like the location of hospitals, community practices, specialist medical and allied services, etc. in a geographic area

  • Geospatial distribution maps superimpose a variety of different data onto a map and visually highlight their linkages. For example, researchers have found a strong relationship between socioeconomic characteristics, the distribution of fast food outlets, and obesity rates across different suburbs

  • Creating geospatial maps for different time periods can show progress in combating particular issues of concern like the improvements in the battle against sleeping sickness in the Democratic Republic of the Congo

Looking forward takes account of the fact that the system as a whole, rather than its discrete entities, produce the behaviours and outcomes one observes and desires. Looking forward is the realm of modelling. A model is a “simplified version of reality”, it contains those “agreed upon” variables regarded as “responsible to cause” the behaviours and outcomes of the system.

Modelling in the first instance is a learning tool—it allows all involved in model building to gain a deep insight into the system’s structure and function. In the second instance it is a decision-making tool—it invites the exploration of “what-if” scenarios to help find the “best possible” approach to solving a problem. Modelling provides decision makers a “safe space” to explore the long-term effects of potential solutions on the system’s behaviours and outcomes.

However, modelling is not a panacea; as Rittel emphasised, every solution to a wicked problem is a “one shot solution”; modelling helps decision makers to “give it the best shot possible”.

Supplementary material


  1. 1.
    Padula W, Duffy M, Yilmaz T, Mishra M (2014) Integrating systems engineering practice with health-care delivery. Health Syst. 3(3):159–164CrossRefGoogle Scholar
  2. 2.
    Haque W, Urquhart B, Berg E, Dhanoa R (2014) Using business intelligence to analyze and share health system infrastructure data in a rural health authority. JMIR Med Inform 2(2):e16CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Scoville R, Little K (2014) Comparing lean and quality improvement. IHI white paper. Institute for Healthcare Improvement, Cambridge, MAGoogle Scholar
  4. 4.
    Hudson P (2003) Applying the lessons of high risk industries to health care. Qual Saf Health Care 12(suppl 1):i7–i12CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Sheppard F, Williams M, Klein VR (2013) TeamSTEPPS and patient safety in healthcare. J Healthc Risk Manag 32(3):5–10CrossRefPubMedGoogle Scholar
  6. 6.
    Marshall DA, Burgos-Liz L, Ijzerman MJ, Osgood ND, Padula WV, Higashi MK, et al (2015) Applying dynamic simulation modeling methods in health care delivery research—the SIMULATE checklist: report of the ISPOR simulation modeling emerging good practices task force. Value Health 18(1):5–16CrossRefPubMedGoogle Scholar
  7. 7.
    Rouse WB (2008) Health care as a complex adaptive system: implications for design and management. The Bridge 38(1):17–25Google Scholar
  8. 8.
    Salvador-Carulla L, Amaddeo F, Gutiérrez-Colosía MR, Salazzari D, Gonzalez-Caballero JL, Montagni I, et al (2015) Developing a tool for mapping adult mental health care provision in Europe: the REMAST research protocol and its contribution to better integrated care. Int J Integr Care 15:e042PubMedPubMedCentralGoogle Scholar
  9. 9.
    Lakes T, Burkart K (2016) Childhood overweight in Berlin: intra-urban differences and underlying influencing factors. Int J Health Geogr 15(1):1–10CrossRefGoogle Scholar
  10. 10.
    Lumbala C, Simarro PP, Cecchi G, Paone M, Franco JR, Kande Betu Ku Mesu V, et al (2015) Human African trypanosomiasis in the Democratic Republic of the Congo: disease distribution and risk. Int J Health Geogr 14:20CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Higgins G, Freedman J (2013) Improving decision making in crisis. J Bus Contin Emer Plan 7(1):65–76PubMedGoogle Scholar
  12. 12.
    Snowden DJ (2005) Multi-ontology sense making: a new simplicity in decision making. Inform Prim Care 13(1):45–53PubMedGoogle Scholar
  13. 13.
    Lynch C, Padilla J, Diallo S, Sokolowski J, Banks C (2014) A multi-paradigm modeling framework for modeling and simulating problem situations. In: Tolk A, Diallo SY, Ryzhov IO, Yilmaz L, Buckley S, Miller JA (eds) Proceedings of the 2014 Winter Simulation Conference: IEEE, p 1688–1699Google Scholar
  14. 14.
    Lakes T, Burkart K (2016) Childhood overweight in Berlin: intra-urban differences and underlying influencing factors. Int J Health Geogr 15(1):1–10CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Joachim P. Sturmberg
    • 1
  1. 1.University of NewcastleWamberalAustralia

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