Research On Healthcare Failure Mode And Effect Analysis Under Complex Uncertain Environment

As an important early preventive measure,failure mode and effect analysis(FMEA)can find the potential failure mode in the system before medical failure.The traditional FMEA usually calculates the risk priority number(PRN)and sorts it to get the result of fault mode importance.However,at present,the structure and function of medical products are becoming more and more complex,which requires more diversity of evaluation,and often requires FMEA method to be used in complex evaluation environment.The inherent defects of the traditional FMEA method limit its further development and application in the medical field.Therefore,in view of the shortcomings of traditional FMEA methods,this dissertation puts forward a new FMEA model which can be used in the complex medical uncertain environment,and further demonstrates its reliability and effectiveness with practical cases.The main work of this dissertation is as follows:(1)Although the number of publications on the application of FMEA to healthcare risk analysis has increased dramatically in the past decades,there is no investigation devoted to review the relevant studies on this topic until now.Therefore,the dissertation systematically reviews the relevant literature which employed FMEA technique for quality improvement and error reduction in the healthcare environment.A total of 158 scientific articles published in refereed journals from 1998 to 2018 are investigated.(2)A new FMEA model considering FMEA team members' vagueness and uncertainty in their evaluations on failure modes,2-dimensional uncertain linguistic variables are advised to describe the risk evaluation result of a failure mode and the reliability of the evaluation result.The grey relation analysis-technique for order preference by similarity to ideal solution(GRA-TOPSIS)is applied for determining the risk ranking of the identified failure modes.In particular,a maximizing deviation method is employed for calculating the optimal weights of risk factors in an objective way.Via a practical healthcare risk analysis case,the new FMEA is proved to be appropriate and effective in coping with the risk evaluation problems with uncertain linguistic information.Furthermore,by comparing with existing methods,it is shown that the proposed integrated approach excels in the risk evaluation and prioritization of failure modes in FMEA.(3)A novel FMEA method which presents a novel risk priority model for FMEA by using interval 2-tuple linguistic variables and an integrated multi-criteria decision making(MCDM)method.The interval 2-tuple linguistic variables are used to capture FMEA team members' diverse assessments on the risk of failure modes and the weights of risk factors.An integrated MCDM method based on regret theory and TODIM(an acronym in Portuguese for interactive MCDM)is developed to prioritize failure modes taking experts' psychological behaviors into account.Finally,an illustrative example regarding medical product development is included to verify the feasibility and effectiveness of the proposed FMEA.By comparing with other existing methods,the proposed linguistic FMEA approach is shown to be more advantageous in ranking failure modes under the uncertain and complex environment.(4)An integrated FMEA method based on fuzzy evidential reasoning(FER)and fuzzy Petri nets(FPNs)to improve the classical FMEA.In this model,belief structures are used to capture the uncertainty and fuzziness of the subjective assessments given by experts and a rule-based FPN model is established to determine the risk priority of the failure modes identified in FMEA.An empirical case concerning the risk evaluation of a blood transfusion process is provided to illustrate the practicality and effectiveness of the proposed FMEA.The results show that the new risk assessment method can produce more reliable risk ranking results of failure modes.