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

Full-text: Access denied (no subscription detected)

We're sorry, but we are unable to provide you with the full text of this article because we are not able to identify you as a subscriber. If you have a personal subscription to this journal, then please login. If you are already logged in, then you may need to update your profile to register your subscription. Read more about accessing full-text

Abstract

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

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

Dates
First available in Project Euclid: 1 October 2014

Permanent link to this document
https://projecteuclid.org/euclid.jam/1412176993

Digital Object Identifier
doi:10.1155/2014/865241

Citation

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. https://projecteuclid.org/euclid.jam/1412176993


Export citation

References

  • R. Y. Shtykh and Q. Jin, “A human-centric integrated approach to web information search and sharing,” Human-Centric Computing and Information Sciences, vol. 1, p. 2, 2011.
  • N. Y. Yen and S. Y. F. Kuo, “An intergrated approach for internet resources mining and searching,” Journal of Convergence, vol. 3, no. 2, pp. 37–44, 2012.
  • D. Hubbard, The Failure of Risk Management: Why It's Broken and How to Fix It, John Wiley & Sons, New York, NY, USA, 2009.
  • ISO, ISO/IEC Guide 73:2009 Risk Management-Vocabulary, 2009.
  • H. VanCott, “Human errors: their causes and reduction,” in Human Error in MEdicine, M. S. Bogner, Ed., pp. 82–98, Lawrence Erlbaum Associates, Hillsdale, NJ, USA, 1994.
  • S. Ternov, “The human side of medical mistakes,” in Error Reduction in Health Care: A Systems Approach to Improving Patient Safty, P. L. Spath, Ed., pp. 97–138, AHA Press, Chicago, Ill, USA, 2002.
  • American Hospital Association, Hospital Statistics, American Hospital Association, Chicago, Ill, USA, 1999.
  • Centers for Disease Control and Prevention-National Center for Health Statistics, “Births and deaths: preliminary data for 1998,” National Vital Statistics Reports, vol. 47, no. 25, p. 6, 1999.
  • T. S. Lesar, B. M. Lomaestro, and H. Pohl, “Medication-prescribing errors in a teaching hospital: A 9-year experience,” Archives of Internal Medicine, vol. 157, no. 14, pp. 1569–1576, 1997.
  • E. J. Thomas, D. M. Studdert, H. R. Burstin et al., “Incidence and types of adverse events and negligent care in Utah and Colorado,” Medical Care, vol. 38, no. 3, pp. 261–271, 2000.
  • L. T. Kohn, J. M. Corrigan, and M. S. Donaldson, Eds., To Err Is Human: Building a Safer Health System, Institute of Medicine, National Academy Press, Washington, DC, USA, 2000.
  • BS5760:Part5, Reliability of Systems, Equipment and Components. Guide to Failure Modes, Effects and Criticality Analysis, 1991.
  • D. Werth, A. Emrich, and A. Chapko, “Prosumerization of mobile service provision: a conceptual approach,” International Journal of Web Portals, vol. 3, no. 4, pp. 44–55, 2011.
  • J. K.-Y. Ng, “Ubiquitous healthcare: healthcare systems and applications enabled by mobile and wireless technologies,” Journal of Convergence, vol. 3, no. 2, pp. 15–20, 2012.
  • A. K. Dey and D. Estrin, “Perspectives on pervasive health from some of the field's leading researchers,” IEEE Pervasive Computing, vol. 10, no. 2, pp. 4–7, 2011.
  • W. Kaiser and M. Sarrafzadeh, “Introduction to special issue on wireless health,” Transactions on Embedded Computing Systems, vol. 10, no. 1, article 10, 2010.
  • S. Deng, C. Youn, Q. Liu, H. Y. Kim, T. Yu, and Y. H. Kim, “Policy adjuster-driven grid workflow management for collaborative heart disease identification system,” Journal of Information Processing Systems, vol. 4, no. 3, pp. 103–112, 2008.
  • M. Lee, J.-W. Lee, K.-A. Kim, and S. S. Park, “Evaluating service description to guarantee quality of U-service ontology,” Journal of Information Processing Systems, vol. 7, no. 2, pp. 287–298, 2011.
  • J. C. Augusto, V. Callaghan, D. Cook, A. Kameas, and I. Satoh, “Intelligent environments: a manifesto,” Human-Centric Computing and Information Sciences, vol. 3, p. 12, 2013.
  • AIAG, A.I.A.G., Potential Failure Mode and Effects Analysis (FMEA) Reference Manual, AIAG, Southfield, Mich, USA, 2nd edition, 1995.
  • JCAHO, J.C.o.A.o.H.O., Hospital accreditation standards, in Oak Brook Terrace (IL): Joint Commission Resources 2006. pp. 255-256, 261–277.
  • P. L. Spath, “Using failure mode and effects analysis to improve patient safety,” AORN Journal, vol. 78, no. 1, pp. 16–41, 2003.
  • M. Ben-Daya and A. Raouf, “A revised failure mode and effects analysis model,” International Journal of Quality & Reliability Management, vol. 13, no. 1, pp. 43–47, 1996.
  • J. B. Bowles, “An assessment of RPN prioritization in a failure modes effects and criticality analysis,” Journal of the IEST, vol. 47, pp. 51–56, 2004.
  • M. Braglia, M. Frosolini, and R. Montanari, “Fuzzy TOPSIS approach for failure mode, effects and criticality analysis,” Quality & Reliability Engineering International, vol. 19, no. 5, pp. 425–443, 2003.
  • C.-L. Chang, P.-H. Liu, and C.-C. Wei, “Failure mode and effects analysis using grey theory,” Integrated Manufacturing Systems, vol. 12, no. 3, pp. 211–216, 2001.
  • W. Gilchrist, “Modelling failure modes and effects analysis,” International Journal of Quality & Reliability Management, vol. 10, no. 5, pp. 16–23, 1993.
  • A. Pillay and J. Wang, “Modified failure mode and effects analysis using approximate reasoning,” Reliability Engineering and System Safety, vol. 79, no. 1, pp. 69–85, 2003.
  • N. R. Sankar and B. S. Prabhu, “Modified approach for prioritization of failures in a system failure mode and effects analysis,” International Journal of Quality & Reliability Management, vol. 18, no. 3, pp. 324–335, 2001.
  • http://www.palisade.com/.
  • C. Ruggiero, R. Sacile, and M. Giacomini, “Home telecare,” Journal of Telemedicine and Telecare, vol. 5, no. 1, pp. 11–17, 1999.
  • L.-C. Chen, C. W. Chen, Y. C. Weng et al., “An information technology framework for strengthening telehealthcare service delivery,” Telemedicine Journal and e-Health, vol. 18, no. 8, pp. 596–603, 2012.
  • N. Howard and E. Cambria, “Intention awareness: improving upon situation awareness in humancentric environments,” Human-Centric Computing and Information Sciences, vol. 3, no. 9, 2013.
  • M. Brahami, B. Atmani, and N. Matta, “Dynamic knowledge mapping guided by data mining: application on healthcare,” Journal of Information Processing Systems, vol. 9, no. 1, pp. 1–30, 2013.
  • L. Prinz, M. Cramer, and A. Englund, “Telehealth: a policy analysis for quality, impact on patient outcomes, and political feasibility,” Nursing Outlook, vol. 56, no. 4, pp. 152–158, 2008.
  • M. S. H. Talpur, “The appliance pervasive of internet of things in healthcare systems,” International Journal of Computer Science Issues, vol. 10, no. 1, pp. 419–424, 2013.
  • J. Basilakis, N. H. Lovell, S. J. Redmond, and B. G. Celler, “Design of a decision-support architecture for management of remotely monitored patients,” IEEE Transactions on Information Technology in Biomedicine, vol. 14, no. 5, pp. 1216–1226, 2010.
  • A. Kailas and M. A. Ingram, “Wireless communications technology in telehealth systems,” in Proceedings of the 1st International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace and Electronic Systems Technology, Wireless (VITAE '09), pp. 926–930, May 2009.
  • R. D. Berndt, M. C. Takenga, S. Kuehn, P. Preik, G. Sommer, and S. Berndt, “SaaS-platform for mobile health applications,” in Proceedings of the 9th International Multi-Conference on Systems, Signals and Devices (SSD '12), pp. 1–4, 2012.
  • A. Kuusik, E. Reilent, I. Lõõbas, and M. Parve, “Software architecture for modern telehome care systems,” in Proceedings of the 6th International Conference on Networked Computing (INC '10), pp. 326–331, May 2010. \endinput