Journal of Applied Mathematics

  • J. Appl. Math.
  • Volume 2014, Special Issue (2014), Article ID 865241, 17 pages.

Adaptive Failure Identification for Healthcare Risk Analysis and Its Application on E-Healthcare

Kuo-Chung Chu and Lun-Ping Hung

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To satisfy the requirement for diverse risk preferences, we propose a generic risk priority number (GRPN) function that assigns a risk weight to each parameter such that they represent individual organization/department/process preferences for the parameters. This research applies GRPN function-based model to differentiate the types of risk, and primary data are generated through simulation. We also conduct sensitivity analysis on correlation and regression to compare it with the traditional RPN (TRPN). The proposed model outperforms the TRPN model and provides a practical, effective, and adaptive method for risk evaluation. In particular, the defined GRPN function offers a new method to prioritize failure modes in failure mode and effect analysis (FMEA). The different risk preferences considered in the healthcare example show that the modified FMEA model can take into account the various risk factors and prioritize failure modes more accurately. In addition, the model also can apply to a generic e-healthcare service environment with a hierarchical architecture.

Article information

J. Appl. Math., Volume 2014, Special Issue (2014), Article ID 865241, 17 pages.

First available in Project Euclid: 1 October 2014

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Chu, Kuo-Chung; Hung, Lun-Ping. Adaptive Failure Identification for Healthcare Risk Analysis and Its Application on E-Healthcare. J. Appl. Math. 2014, Special Issue (2014), Article ID 865241, 17 pages. doi:10.1155/2014/865241.

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