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

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Title: Adaptive Failure Identification for Healthcare Risk Analysis and Its Application on E-Healthcare.
Authors: Kuo-Chung Chu1 kcchu@ntunhs.edu.tw, Lun-Ping Hung1
Source: Journal of Applied Mathematics. 2014, p1-17. 17p.
Subjects: Adaptive computing systems, Failure Analysis System (Computer system), Medical care, Number theory, Computer simulation, Data analysis
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-basedmodel 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. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Applied Mathematics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Adaptive Failure Identification for Healthcare Risk Analysis and Its Application on E-Healthcare.
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  Data: <searchLink fieldCode="AR" term="%22Kuo-Chung+Chu%22">Kuo-Chung Chu</searchLink><relatesTo>1</relatesTo><i> kcchu@ntunhs.edu.tw</i><br /><searchLink fieldCode="AR" term="%22Lun-Ping+Hung%22">Lun-Ping Hung</searchLink><relatesTo>1</relatesTo>
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  Data: <searchLink fieldCode="DE" term="%22Adaptive+computing+systems%22">Adaptive computing systems</searchLink><br /><searchLink fieldCode="DE" term="%22Failure+Analysis+System+%28Computer+system%29%22">Failure Analysis System (Computer system)</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+care%22">Medical care</searchLink><br /><searchLink fieldCode="DE" term="%22Number+theory%22">Number theory</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+simulation%22">Computer simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink>
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  Data: 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-basedmodel 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Applied Mathematics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1155/2014/865241
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        Text: English
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        PageCount: 17
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      – SubjectFull: Adaptive computing systems
        Type: general
      – SubjectFull: Failure Analysis System (Computer system)
        Type: general
      – SubjectFull: Medical care
        Type: general
      – SubjectFull: Number theory
        Type: general
      – SubjectFull: Computer simulation
        Type: general
      – SubjectFull: Data analysis
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      – TitleFull: Adaptive Failure Identification for Healthcare Risk Analysis and Its Application on E-Healthcare.
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            NameFull: Lun-Ping Hung
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              Text: 2014
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            – TitleFull: Journal of Applied Mathematics
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