Importance Measures in Reliability Analysis of Healthcare System

  • E. Zaitseva
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 98)


Healthcare system is modern complex system that includes four principal components in point of view of reliability engineering. They are hardware, software, human factor and organization component. There are different methods in reliability engineering for analysis and quantification of every of these components. But new tendency in reliability analysis needs methods that evaluate the system as a single whole. In accordance with this tendency one aspect of reliability engineering (importance analysis) is considered in the paper. The importance reliability analysis allows to estimate influence of every healthcare system component to the system reliability and functioning. New algorithms for importance analysis of healthcare system are proposed in this paper.


Healthcare System Reliability Analysis System Reliability Component State Importance Measure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. [Aven and Nokland 2010]
    Aven, T., Nokland, T.E.: On the use of uncertainty importance measures in reliability and risk analysis. Reliability Engineering and System Safety 95(2), 127–133 (2010)CrossRefGoogle Scholar
  2. [Cohen 2004]
    Cohen, T.: Medical and information technologies converge. IEEE Engineering in Medicine and Biology Magazine 23(3), 59–65 (2004)CrossRefGoogle Scholar
  3. [Fricks and Trivedi 2003]
    Fricks, R.M., Trivedi, K.S.: Importance analysis with Markov Chains. In: Proc. IEEE the 49th Annual Reliability & Maintainability Symposium, Tampa, USA, pp. 89–95 (2003)Google Scholar
  4. [Lisnianski and Levitin 2003]
    Lisnianski, A., Levitin, G.: Multi-state system reliability. Assessment, optimization and applications, p. 358. World Scientific, Singapore (2003)zbMATHGoogle Scholar
  5. [Lyons et al. 2004]
    Lyons, M., Adams, S., Woloshynowych, M., Vincent, C.: Human reliability analysis in healthcare: A review of techniques. Int. Journal of Risk & Safety in Medicine 16(4), 223–237 (2004)Google Scholar
  6. [Marseguerra andZio 2004]
    Marseguerra, M., Zio, E.: Monte Carlo estimation of the differential importance measure: application to the protection system of a nuclear reactor. Reliability Engineering and System Safety 86(1), 11–24 (2004)CrossRefGoogle Scholar
  7. [Pham 2003]
    Pham, H. (ed.): Handbook of Reliability Engineering, p. 659. Springer, London (2003)Google Scholar
  8. [Taleb-Bendiab et al. 2006]
    Taleb-Bendiab, A., England, D., Randles, M., et al.: A principled approach to the design of healthcare systems: Autonomy vs. governance. Reliability Engineering and System Safety 91(12), 1576–1585 (2006)CrossRefGoogle Scholar
  9. [Taylor 1972]
    Taylor, E.F.: The reliability engineer in the health care system. In: Proc. IEEE the 18th Annual Reliability & Maintainability Symposium, USA, pp. 245–248 (1972)Google Scholar
  10. [Zaitseva 2009]
    Zaitseva, E.: Importance analysis of Multi-State System by tools of Differential Logical Calculus. In: Bris, R., et al. (eds.) Reliability, Risk and Safety. Theory and Applications, vol. 3, pp. 1579–1584. CRC Press, Boca Raton (2009)Google Scholar
  11. [Zaitseva2010]
    Zaitseva, E.: Reliability Analysis Methods for Healthcare system. In: Proc. IEEE the 3rd Int Conf. on Human System Interaction, Rzeszow, Poland, pp. 212–216 (2010)Google Scholar
  12. [Zio 2009]
    Zio, E.: Reliability engineering: Old problems and new challenges. Reliability Engineering and System Safety 94(2), 125–141 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • E. Zaitseva
    • 1
  1. 1.Department of InformaticsUniversity of ZilinaZilinaSlovakia

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