A New Fuzzy AHP- Fuzzy VIKOR Approach in Control and Management of The Angiography Procedure to Prevent Disruptions: A Case Study

Document Type : Research Paper


1 Industral engineering department, Semnan university, Semnan, Iran

2 Industrial engineering department, Semnan university,Semnan, Iran

3 Cancer Research Center, Anesthesiology Department, Semnan University of Medical Sciences


Background and objectives - A hybrid MCDM approach is presented to evaluate and prioritize the disruptions in the angiography process, in a fuzzy environment. The proposed approach is applied to a real case in a public hospital.
Methods – In this study, a new approach is utilized based on fuzzy MCDM methods. The disruptions are identified using the experts' opinions. Then, the FMEA risk factors are compared in pairs and given weights by experts, using fuzzy AHP. The Experts were then asked to rate the disruptions according to the risk factors. Finally, the disruptions were ranked using Fuzzy VIKOR.
Results and Conclusion - Results show that the risk factor occurrence has the most importance among the three risk factors. They also suggest that the top three disruptions in the angiography process are ‘absence of manual’ and ‘guideline on angiography procedure’, ‘inadequate training of personnel and exhaustion’, respectively.
Practical implications - Results of this study may be may help hospital managers and practitioners avoid disruptions in the process and the improve healthcare service quality.
Originality/value - The recent studies in the related literature were thoroughly investigated and it was found that no studies considered the disruptions identification and analysis in the angiography process. Therefore, the disruptions in the angiography process are investigated for the first time. Moreover, the efficiency and applicability of the proposed method and the rankings are validated by the experts.


Yaghoubi, A., et al., Quality of Life in Cardiovascular Patients in Iran and Factors Affecting It: A Systematic Review. Journal of cardiovascular and thoracic research, 2012. 4(4): p. 95-101.
2.            Moradi, B., et al., Factors Associated With Failure to Complete Phase II Cardiac Rehabilitation: Survey Registry in Rajaie Cardiovascular Medical and Research Center. Int Cardiovasc Res J, 2011. 5(4): p. 139-142.
3.            Jamshidi, A., et al., A comprehensive fuzzy risk-based maintenance framework for prioritization of medical devices. Applied Soft Computing, 2015. 32: p. 322-334.
4.            Raghavan, V., et al., Reengineering the Cardiac Catheterization Lab Processes: A Lean Approach. Journal of Healthcare Engineering, 2010. 1(1).
5.            Gupta, U., et al., Failure Mode Effect Analysis in Healthcare - Preventing An Error before any Harm is Done. Apollo Medicine, 2004. 1(1): p. 64-68.
6.            Beheshtinia, M.A. and S. Omidi, A hybrid MCDM approach for performance evaluation in the banking industry. Kybernetes, 2017. 46(8): p. 1386-1407.
7.            Sedady, F. and M.A. Beheshtinia, A novel MCDM model for prioritizing the renewable power plants’ construction. Management of Environmental Quality: An International Journal, 2019. 30(2): p. 383-399.
8.            Beheshtinia, M. and V. Nemati-Abozar, A Novel Hybrid Fuzzy Multi-Criteria Decision-Making Model for Supplier Selection Problem (A Case Study in Advertising industry). Journal of Industrial and Systems Engineering, 2017. 9(4): p. 65-79.
9.            Rezaee, M.S., A. Haeri, and S. Noori, Using data envelopment analysis to evaluate the performances of food production companies based on EFQM's criteria and to present an improvement plan. International Journal of Business Excellence, 2018. 14(2): p. 256-274.
10.          Haeri, A. and K. Rezaie, Using data envelopment analysis to investigate the efficiency of resource utilisation and to develop an improvement plan. International Journal of Productivity and Quality Management, 2014. 13(1): p. 39-66.
11.          Haeri, A., K. Rezaie, and M.-S. Amalnick, Developing a novel approach to assess the efficiency of resource utilisation in organisations: a case study for an automotive supplier. International Journal of Production Research, 2014. 52(10): p. 2815-2833.
12.          Shafii, M., et al., Performance analysis of hospital managers using fuzzy AHP and fuzzy TOPSIS: Iranian Experience. Global journal of health science, 2016. 8(2): p. 137.
13.          Chang, T.-H., Fuzzy VIKOR method: A case study of the hospital service evaluation in Taiwan. Information Sciences, 2014. 271: p. 196-212.
14.          Chanamool, N. and T. Naenna, Fuzzy FMEA application to improve decision-making process in an emergency department. Applied Soft Computing, 2016. 43: p. 441-453.
15.          Liu, H.-C., et al., Risk evaluation in failure mode and effects analysis with extended VIKOR method under fuzzy environment. Expert Systems with Applications, 2012. 39(17): p. 12926-12934.
16.          Liu, H.-C., et al., A novel approach for failure mode and effects analysis using combination weighting and fuzzy VIKOR method. Applied Soft Computing, 2015. 28: p. 579-588.
17.          Torkzad, A. and M.A. Beheshtinia, Evaluating and prioritizing hospital service quality. International Journal of Health Care Quality Assurance, 2019. 32(2): p. 332-346.
18.          Al-Hakim, L., Surgical disruption: information quality perspective. International Journal of Information Quality, 2008. 2(2): p. 192-204.
19.          Abuzeid, W., et al., Radiation safety in the cardiac catheterization lab: A time series quality improvement initiative. Cardiovascular Revascularization Medicine, 2017. 18(5, Supplement 1): p. S22-S26.
20.          Jayakar, J.P. and D.A. Alter, Music for anxiety reduction in patients undergoing cardiac catheterization: A systematic review and meta-analysis of randomized controlled trials. Complementary Therapies in Clinical Practice, 2017. 28: p. 122-130.
21.          Azami-Aghdash, S., et al., Customer Quality: A Self-reporting Survey among Angiography Patients. International Journal of Hospital Research, 2013. 2(3): p. 119-126.
22.          Zadeh, L.A., Fuzzy sets. Information and Control, 1965. 8(3): p. 338-353.
23.          Zadeh, L.A., The concept of a linguistic variable and its application to approximate reasoning—II. Information Sciences, 1975. 8(4): p. 301-357.
24.          Liu, H.-C., L. Liu, and N. Liu, Risk evaluation approaches in failure mode and effects analysis: A literature review. Expert Systems with Applications, 2013. 40(2): p. 828-838.
25.          Kutlu, A.C. and M. Ekmekçioğlu, Fuzzy failure modes and effects analysis by using fuzzy TOPSIS-based fuzzy AHP. Expert Systems with Applications, 2012. 39(1): p. 61-67.
26.          Reiling, J.G., B.L. Knutzen, and S. M, FMEA—the Cure for Medical Errors. Quality Progress, 2003: p. 67-71.
27.          Braglia, M., M. Frosolini, and R. Montanari, Fuzzy TOPSIS approach for failure mode, effects and criticality analysis. Quality and Reliability Engineering International, 2003. 19(5): p. 425-443.
28.          Bowles, J.B. and C.E. Peláez, Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis. Reliability Engineering and System Safety, 1995. 50: p. 203-213.
29.          Gumus, A.T., Evaluation of hazardous waste transportation firms by using a two step fuzzy-AHP and TOPSIS methodology. Expert Systems with Applications, 2009. 36(2, Part 2): p. 4067-4074.
30.          Chang, D.-Y., Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 1996. 95(3): p. 649-655.
31.          Hu, S.-K., M.-T. Lu, and G.-H. Tzeng, Exploring smart phone improvements based on a hybrid MCDM model. Expert Systems with Applications, 2014. 41(9): p. 4401-4413.
32.          Padilla-Garrido, N., et al., Multicriteria Decision Making in Health Care Using the Analytic Hierarchy Process and Microsoft Excel. Medical Decision Making, 2014. 34(7): p. 931-935.