New Methods for the Reliability Analysis of Healthcare System Based on Application of Multi-State System

Part of the Studies in Computational Intelligence book series (SCI, volume 606)


A healthcare system is complex and high-risk. Therefore reliability analysis of a healthcare system is principal step in its development and exploitation. The high-risk of a healthcare system is caused by different factors as human error, failure of devices and equipment, software fault, etc. These factors correlate with complex structure of a healthcare system that consists of technical and human parts. But as a rule in reliability engineering the analysis and estimation of technical components and human factor are implemented based on different methods that have different mathematical backgrounds. One of possible decision of this problem is development of new mathematical model, that allows to describe booth as technical components as human factors. Such model can be defined based on representation of a healthcare system as Multi-state System, for which can be define some (more that only two) performance levels.


Healthcare System Structure Function Reliability Analysis Medical Error System Reliability 
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.



This work is supported by the grant of 7th RTD Framework Programme No 610425 (RASimAs) and grant of Scientific Grant Agency of the Ministry of Education of Slovak Republic (Vega 1/0498/14).


  1. 1.
    Armstrong, M.: Reliability-importance and dual failure-mode components. IEEE Trans. Reliab. 46(2), 212–221 (1997)CrossRefGoogle Scholar
  2. 2.
    Birnbaum, Z.W.: On importance of difference components in a multi-component system. Multi-Variant Anal 2, 581–592 (1969)MathSciNetGoogle Scholar
  3. 3.
    Butler, D.A.: A complete importance ranking for components of binary coherent systems with extensions to multi-state systems. Naval Res. Logistics 4, 565–578 (1979)CrossRefGoogle Scholar
  4. 4.
    Caldarola, L.: Coherent system with multi-state components. Nucl. Eng. Design 58(5), 127–139 (1980)CrossRefGoogle Scholar
  5. 5.
    Cohen, T.: Medical and information technologies converge. IEEE Eng. Med. Biol. Mag. 23(3), 59–65 (2004)CrossRefGoogle Scholar
  6. 6.
    Contini, S., Matuzas, V.: New methods to determine the importance measures of initiating and enabling events in fault tree analysis. Reliab. Eng. Syst. Saf. 96(7), 775–784 (2011)CrossRefGoogle Scholar
  7. 7.
    Deeter, J., Rantanen, E.: Human reliability analysis in healthcare. In: Proceedings of Symposium on Human Factors and Ergonomics in Health Care, pp. 45–51. Baltimore, USA (2012)Google Scholar
  8. 8.
    Dhillon, B.S.: Medical Device Reliability and Associated Areas. CRC Press, p. 264 (2000)Google Scholar
  9. 9.
    Dhillon, B.S.: Human Reliability and Error in Medical System. World Scientific, p. 233 (2003)Google Scholar
  10. 10.
    Dixon, N.M., Shofer, M.: Struggling to invent high-reliability organizations in health care settings: insights from the field. Health Serv. Res. 41(4), 1618–1632 (2006)CrossRefGoogle Scholar
  11. 11.
    Farcasiu, M., Prisecaru, I.: MMOSA—A new approach of the human and organizational factor analysis in PSA. Reliab. Eng. Syst. Saf. 123(1), 91–98 (2014)CrossRefGoogle Scholar
  12. 12.
    Fichman, R.G., Kohli, R., Krishnan, R.: The role of information system in healthcare: current research and future trends. Inf. Syst. Res. 22(3), 419–427 (2011)CrossRefGoogle Scholar
  13. 13.
    Hudson, J.C., Kapur, K.C.: Modules in coherent multistate systems. IEEE Trans. Reliab. 32(2), 183–185 (1983)zbMATHCrossRefGoogle Scholar
  14. 14.
    Hellmich, M., Berg, H.P.: Markov analysis of redundant standby safety systems under periodic surveillance testing. Reliab. Eng. Syst. Saf. 133(4), 48–58 (2015)CrossRefGoogle Scholar
  15. 15.
    Kuo, W., Zhu, X.: Importance Measures in Reliability, Risk, and Optimization. Wiley, p. 472 (2012)Google Scholar
  16. 16.
    Li, Y., Zio, E.: A multi-state model for the reliability assessment of a distributed generation system via universal generating function. Reliab. Eng. Syst. Saf. 106(1), 28–36 (2012)CrossRefGoogle Scholar
  17. 17.
    Lisnianski, A., Levitin, G.: Multi-State System Reliability. Assessment, Optimization and Applications. World scientific, p. 358 (2003)Google Scholar
  18. 18.
    Lisnianski, A., Elmakias, D., Laredo, D., Haim, H.B.: A multi-state Markov model for a short-term reliability analysis of a power generating unit. Reliab. Eng. Syst. Saf. 98(2), 1–6 (2012)CrossRefGoogle Scholar
  19. 19.
    Lyons, M., Adams, S., Woloshynowych, M., Vincent C.: Human reliability analysis in healthcare: a review of techniques. Int. J. Risk Saf. Med. 16(4), 223–237 (2004)Google Scholar
  20. 20.
    Mohaghegh, Z., Kazemi, R., Mosleh, A.: Incorporating organizational factors into Probabilistic Risk Assessment (PRA) of complex socio-technical systems: a hybrid technique formalization original research article. Reliab. Eng. Syst. Saf. 94(5), 1000–1018 (2009)CrossRefGoogle Scholar
  21. 21.
    Meng, F.C.: On some structural importance of system components. J. Data Sci. 7, 277–283 (2009)Google Scholar
  22. 22.
    Merle, G., Roussel, J.M., Lesage, J.J.: Algebraic determination of the structure function of dynamic fault trees. Reliab. Eng. Syst. Saf. 96(2), 267–277 (2011)CrossRefGoogle Scholar
  23. 23.
    Natvig, B.: Multistate Systems Reliability Theory with Applications. Wiley, New York (2011). 232pzbMATHCrossRefGoogle Scholar
  24. 24.
    Reinske, K., Ushakov, I.: Application of Graph Theory for Reliability Analysis. Radio i Sviaz, Moscow, p. 208 (1988) (in Russian)Google Scholar
  25. 25.
    Rocha, A.: Evolution of information systems and technologies maturity in healthcare. Int. J. Healthc. Inf. Syst. Inform. 6(2), 28–36 (2011)CrossRefGoogle Scholar
  26. 26.
    Smith, D.J.: Reliability, Maintainability and Risk: Practical Methods for Engineers including Reliability Centred Maintenance and Safety-Related Systems. Butterworth-Heinemann Ltd, Oxford, p. 436 (2011)Google Scholar
  27. 27.
    Seyed-Hosseini, S.M., Safaei, N., Asgharpour, M.J.: Reprioritization of failures in a system failure mode and effects analysis by decision making trial and evaluation laboratory technique. Reliab. Eng. Syst. Saf. 91(8), 872–881 (2006)CrossRefGoogle Scholar
  28. 28.
    Shooman, M.L.: Probabilistic Reliability: An Engineering Approach. McGraw-Hill, New York (1968)Google Scholar
  29. 29.
    Spyrou, S., Bamidis, P.D., Maglaveras, N., Pangalos, G., Pappas, C.: A methodology for reliability analysis in health networks. IEEE Trans. Inf. Technol. Biomed. 12(3), 377–386 (2008)Google Scholar
  30. 30.
    Taleb-Bendiab, A., England, D., Randles, M., Miseldine, P., Murphy, K.: A principled approach to the design of healthcare systems: autonomy versus governance. Reliab. Eng. Syst. Saf. 91(12), 1576–1585 (2006)CrossRefGoogle Scholar
  31. 31.
    Taylor, E.F.: The reliability engineer in the health care system. In: Proceedings of IEEE the 18th Annual Reliability and Maintainability Symposium, pp. 245–248, USA (1972)Google Scholar
  32. 32.
    Vaurio, J.K.: Ideas and developments in importance measures and fault-tree techniques for reliability and risk analysis. Reliab. Eng. Syst. Saf. 95(2), 99–107 (2010)CrossRefGoogle Scholar
  33. 33.
    Veeraraghavan, M., Trivedi, K.S.: A combinatorial algorithm for performance and reliability analysis using multistate models. IEEE Trans. Comput. 43(2), 29–233 (1994)CrossRefGoogle Scholar
  34. 34.
    Yu, K., Koren, I., Guo, Y.: Generalized multistate monotone coherent systems. IEEE Trans. Reliab. 43(2), 242–250 (1994)CrossRefGoogle Scholar
  35. 35.
    Zaitseva, E.: Reliability analysis of multi-state system. Dyn. Syst. Geom. Theor. 1(1), 213–222 (2003)Google Scholar
  36. 36.
    Zaitseva, E.: Importance analysis of a multi-state system based on multiple-valued logic methods. In: Lisnianski, A., Frenkel, I. (eds.) Recent Advances in System Reliability: Signatures, pp. 113–134. Springer, Multi-state Systems and Statistical Inference, London (2012)CrossRefGoogle Scholar
  37. 37.
    Zaitseva, E., Levashenko, V., Rusin, M.: Reliability analysis of healthcare system. In: Proceedings of the IEEE Federated Conference on Computer Science and Information Systems, pp. 169–175. Szczecin, Poland (2011)Google Scholar
  38. 38.
    Zaitseva, E., Levashenko, V.: Importance analysis by logical differential calculus. Autom. Remote Control 74(2), 171–182 (2013)zbMATHMathSciNetCrossRefGoogle Scholar
  39. 39.
    Zaitseva, E., Kostolny, J., Kvassay, M., Levashenko, V., Pancerz, K.: Failure Analysis and Estimation of the Healthcare System. In: Proceedings of the IEEE Federated Conference on Computer Science and Information Systems, pp. 235–240. Krakow, Poland (2013)Google Scholar
  40. 40.
    Zaitseva, E., Kostolny, J., Kvassay, M., Levashenko, V., Pancerz, K.: Estimation of a healthcare system based on the importance analysis. In: Pancerz, K., Zaitseva, E. (eds.) Computational Intelligence, Medicine and Biology: Selected Links, pp. 3–22. Springer, London (2015)Google Scholar
  41. 41.
    Zio, E., Marella, M., Podofillini, L.: Importance measures-based prioritization for improving the performance of multi-state systems: application to the railway industry. Reliab. Eng. Syst. Saf. 92(10), 1303–1314 (2007)CrossRefGoogle Scholar
  42. 42.
    Zio, E.: Reliability engineering: old problems and new challenges. Reliab. Eng. Syst. Saf. 94(2), 125–141 (2009)CrossRefGoogle Scholar

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© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of InformaticsUniversity of ZilinaZilinaSlovakia

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