Fuzzy fault tree analysis for patient safety risk modeling in healthcare under uncertainty
Fuzzy fault tree analysis for patient safety risk modeling in healthcare under uncertainty
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DOI：
10.1016/j.asoc.2015.08.005
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Komal
中科院分区：
文献类型：
--
作者：
Komal
Graphical abstractDisplay Omitted HighlightsThe present study analyzes healthcare related systems faults due to medical errors using available uncertain data.A novel fuzzy fault tree analysis(FFTA) has been developed for this purpose which applies fault tree, trapezoidal fuzzy number, alpha-cut set, weakest t-norm (Tw ) based approximate arithmetic operations to find the fuzzy probability of top event.Computed results have been compared with results obtained from existing techniques.Results suggest that newly developed FFTA reduce the accumulating phenomenon of fuzziness by adopting the weakest t-norm ( T w ) based approximate arithmetic operations.Critical components of the system have been ranked by using the Tanaka et al.'s approach. Nowadays healthcare becoming more important aspect for everybody. Healthcare institutions now giving more attention to their patients' safety by reducing the frequency of medical errors and trying to provide all kinds of best facilities to them. Clinical processes can be understood as a series of interactions between patients, providers, and technologies. Therefore, there are some chances exist for medical errors due to the involvement of human beings and machines. A number of tools exist to prospectively analyze processes in healthcare which generally needs precise numerical data. In general, available or extracted data is not precise and sufficient to assess the clinical processes upto a desired degree of accuracy due to various practical and economic reasons. Thus, collected data may have some sort of uncertainties and quantification of these uncertainties should be done very carefully before analysing further. In this paper, a new fuzzy fault tree approach has been presented for patient safety risk modeling in healthcare. This approach applies fault-tree, trapezoidal fuzzy numbers, α-cut set, and the weakest-t-norm (Tω) based approximate arithmetic operations to obtain fuzzy failure probability of the system. The effectiveness of the developed approach is illustrated with two different kinds of problems taken from literature related to healthcare. Also, Tanaka et al.'s approach has been used to rank the critical basic events of the considered problems. Computed results have been compared with results obtained from other existing techniques.
