A Systems Model of HIT-Induced Complexity

  • Craig KuziemskyEmail author
  • Andrea Ghazzawi


Background While health information technology (HIT) is playing a key role in transforming the healthcare system into a collaborative patient centered system, it is common for unintended consequences (UICs) to emerge post-HIT implementation. Healthcare delivery is a complex adaptive system and UICs occur because of multiple interactions between technology, users, organizational policies, and other situational contexts. Understanding the nature of these interactions and the manner in which they occur is a necessary first step to managing UICs from HIT implementation.

Methodology We use a case study of a perioperative system to identify three categories of UICs. We then further analyzed the UICs using complex adaptive systems concepts to articulate the interactions that led to the UICs as well as the upstream and downstream implications of them.

Results From our analysis we developed a systems model of four dimensions of HIT induced complexity: temporal, policy, workflow, and connectivity complexity. It also emphasizes that we cannot think of HIT implementation as an in-the-moment event. Rather, tasks such as information entry or retrieval may have emerging properties and evolve in complexity as the tasks interact with other people, processes, and technologies.

Conclusion Implementing HIT in complex healthcare settings is a significant challenge. While the complexity of healthcare delivery prevents us from predicting the specific interactions that lead to UICs, our systems model of HIT complexity enables us to make inferences about how certain interactions occur and the contexts where they occur. Our model helps our understanding of the complexity of HIT implementation and improves our ability to proactively manage UICs.



We acknowledge funding from a Discovery Grant from the Natural Sciences and Engineering Research Council of Canada and the Research Chair in Healthcare Innovation from the University of Ottawa.


  1. 1.
    Ash JS, Berg M, Coiera E. Some unintended consequences of information technology in health care: the nature of patient care information system-related errors. J Am Med Inform Assoc. 2004;11(2):104–12.CrossRefGoogle Scholar
  2. 2.
    Kuziemsky CE, Randell R, Borycki EM. Understanding unintended consequences and health information technology: Contribution from the IMIA organizational and social issues working group. Yearb Med Inform. 2016(1):53–60.Google Scholar
  3. 3.
    Sturmberg JP, O’Halloran DM, Martin CM. Understanding health system reform - a complex adaptive systems perspective. J Eval Clin Pract. 2012;18(1):202–8.CrossRefGoogle Scholar
  4. 4.
    Coiera E. Interaction design theory. Int J Med Inform. 2003;69(2–3):205–22.CrossRefGoogle Scholar
  5. 5.
    Kuziemsky CE, Andreev P, Benyoucef M, O’Sullivan T, Jamaly S. A connectivity framework for social information systems design in healthcare. AMIA Ann Symp Proc. 2016;2016:734–42.Google Scholar
  6. 6.
    Reid I. Complexity science. Let them eat complexity: the emperor’s new toolkit. BMJ. 2002;324(7330):171.Google Scholar
  7. 7.
    Koppel R. The health information technology safety framework: building great structures on vast voids. BMJ Qual Saf. 2015.Google Scholar
  8. 8.
    Abbott PA, Foster J, Marin HDF, Dykes PC. Complexity and the science of implementation in health IT-knowledge gaps and future visions. Int J Med Inform. 2014;83(7):e12–e22.CrossRefGoogle Scholar
  9. 9.
    Patrick Albert P, Lori TP, Miguel Noe Ramirez N. Health information technology: Anticipating, recognizing, and preventing disruptions in complex adaptive healthcare systems. In: Vaughan M, Deborah JR-L, Stephen RG, Wendy C, editors. Handbook of research on patient safety and quality care through health informatics. Hershey, PA, USA: IGI Global; 2014. p. 214–35.Google Scholar
  10. 10.
    Kannampallil TG, Schauer GF, Cohen T, Patel VL. Considering complexity in healthcare systems. J Biomed Inform. 2011;44(6):943–7.CrossRefGoogle Scholar
  11. 11.
    Harrison MI, Koppel R, Bar-Lev S. Unintended consequences of information technologies in health care—An interactive sociotechnical analysis. J Am Med Inform Assoc. 2007;14(5):542–9.CrossRefGoogle Scholar
  12. 12.
    Borycki E. Trends in health information technology safety: From technology-induced errors to current approaches for ensuring technology safety. Healthcare Inform Res. 2013;19(2):69–78.CrossRefGoogle Scholar
  13. 13.
    Magrabi F, Baker M, Sinha I, Ong MS, Harrison S, Kidd MR, Runciman WB, Coiera E. Clinical safety of England’s national programme for IT: a retrospective analysis of all reported safety events 2005 to 2011. Int J Med Inform. 2015;84(3):198–206.CrossRefGoogle Scholar
  14. 14.
    Magrabi F, Ong MS, Runciman W, Coiera E. An analysis of computer-related patient safety incidents to inform the development of a classification. J Am Med Inform Assoc. 2010;17(6):663–70.CrossRefGoogle Scholar
  15. 15.
    Coiera E, Ash J, Berg M. The unintended consequences of health information technology revisited. Yearb Med Inform. 2016(1):163–9.Google Scholar
  16. 16.
    Kuziemsky CE, Borycki EM, Kushniruk AW. Studying the HIT-complexity interchange. Stud Health Technol Inform. 2016;225:38–42.PubMedGoogle Scholar
  17. 17.
    Lipsitz LA. Understanding health care as a complex system: The foundation for unintended consequences. JAMA. 2012;308(3):243–4.CrossRefGoogle Scholar
  18. 18.
    Kuziemsky CE. Review of social and organizational issues in health information technology. Healthcare Inform Res. 2015;21(3):152–60.CrossRefGoogle Scholar
  19. 19.
    Kuziemsky CE. A model of tradeoffs for understanding health information technology implementation. Stud Health Technol Inform. 2015;215:116–28.PubMedGoogle Scholar
  20. 20.
    Kuziemsky CE, Bush P. Coordination considerations of healthcare information technology. Studies Health Technol Inform. 2013;194:133–8.Google Scholar
  21. 21.
    Widmer MA, Swanson RC, Zink BJ, Pines JM. Complex systems thinking in emergency medicine: A novel paradigm for a rapidly changing and interconnected health care landscape. J Eval Clin Pract. 2018;24(3):629–634.CrossRefGoogle Scholar
  22. 22.
    Ellis B, Herbert SI. Complex adaptive systems (CAS): an overview of key elements, characteristics and application to management theory. Inform Prim Care. 2011;19(1):33–7.PubMedGoogle Scholar
  23. 23.
    Braithwaite J, Churruca K, Long JC, Ellis LA, Herkes J. When complexity science meets implementation science: a theoretical and empirical analysis of systems change. BMC Med. 2018;16:63.CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Telfer School of ManagementUniversity of OttawaOttawaCanada

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