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Gastric esophageal surgery risk analysis with a fault tree and Markov integrated model

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  • Zixian, Liu
  • Xin, Ni
  • Yiliu, Liu
  • Qinglu, Song
  • Yukun, Wang

Abstract

Reliability methods have been widely used in risk analysis of medical surgeries. In this study, the authors combine a fault tree with Markov models to assess time independent- and dependent factors together. Dynamics are integrated in the traditional fault tree, and meanwhile the processes of solving Markov are simplified with the modular approach. Continuous time Markov chains are adopted in evaluating the failure probability of a gastric esophageal surgery after categorizing basic events in the fault tree, and a certain time dependent variables, such as failure rate of medical equipment, surgery frequency, and rescue timeliness are involved into risk analysis. A case is studied with data collected from a general hospital, to illustrate the operational process of the proposed method. Results based on the inputs show that taking rescue actions into consideration can reduce the gap between the result of fault tree analysis and the reality. Sensitivity analysis for measuring the impacts of the above time relevant variables is conducted, as well as limitations of the Markov model are discussed.

Suggested Citation

  • Zixian, Liu & Xin, Ni & Yiliu, Liu & Qinglu, Song & Yukun, Wang, 2011. "Gastric esophageal surgery risk analysis with a fault tree and Markov integrated model," Reliability Engineering and System Safety, Elsevier, vol. 96(12), pages 1591-1600.
    • Handle: RePEc:eee:reensy:v:96:y:2011:i:12:p:1591-1600
    DOI: 10.1016/j.ress.2011.08.004
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    References listed on IDEAS

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    2. Huang, Chin-Yu & Chang, Yung-Ruei, 2007. "An improved decomposition scheme for assessing the reliability of embedded systems by using dynamic fault trees," Reliability Engineering and System Safety, Elsevier, vol. 92(10), pages 1403-1412.
    3. Taleb-Bendiab, A. & England, David & Randles, Martin & Miseldine, Philip & Murphy, Karen, 2006. "A principled approach to the design of healthcare systems: Autonomy vs. governance," Reliability Engineering and System Safety, Elsevier, vol. 91(12), pages 1576-1585.
    4. Nicky J. Welton & A. E. Ades, 2005. "Estimation of Markov Chain Transition Probabilities and Rates from Fully and Partially Observed Data: Uncertainty Propagation, Evidence Synthesis, and Model Calibration," Medical Decision Making, , vol. 25(6), pages 633-645, November.
    5. Frank A. Sonnenberg & J. Robert Beck, 1993. "Markov Models in Medical Decision Making," Medical Decision Making, , vol. 13(4), pages 322-338, December.
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    Cited by:

    1. Yılmaz, Emre & German, Brian J. & Pritchett, Amy R., 2023. "Optimizing resource allocations to improve system reliability via the propagation of statistical moments through fault trees," Reliability Engineering and System Safety, Elsevier, vol. 230(C).

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