Risk assessment of medical devices used for COVID-19 patients based on a Markovian-based Weighted Failure and Mode Effects Analysis (WFMEA)

Document Type : Research Article

Authors

School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.

10.24200/sci.2022.57493.5266

Abstract

Medical devices are critical in the healthcare system and their failures can significantly impress the safety of patients, medical staff, and clinical engineers. With increasing COVID-19 pandemic in recent months, it is more necessary to assess the risks of the devices to avoid infection for patients, death, and severe hurts due to inactive and breakdown devices. The aim of this study is to assess medical device risks in general and pandemic situations with three main factors of the failure model analysis effect include occurrence, detection, and severity. Some sub-factors are defined and weighted using the fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) and fuzzy Best-Worst Method (BWM). Consequently, the Weighted Failure Mode and Effects Analysis (WFMEA) score of each failure is calculated as the Weighted Risk Priority Number (WRPN). Finally, steady-state probabilities of very low and low failures are calculated to consider the changes during the time. Results show that near half of the failures are scored in very low and low levels but in the long term, most of them transfer to medium level risk. It can be concluded that some preventive maintenance plans for these kinds of failures to avoid occurring the higher risk level for them in the future is necessary and the results can help medical device managers.

Keywords

Main Subjects


References:
1. Huang, J., Gao, P., and Guo, E.Y. “The research of medical equipment management based on JCI standard”, Applied Mechanics and Materials, 278, pp. 2226–2231 (2013). https://doi.org/10.4028/www.scientific.net/AMM.2 78-280.2226.
2. Malki, Z., Atlam, E.S., Ewis, A., et al. “ARIMA models for predicting the end of COVID-19 pandemic and the risk of second rebound”, Neural Computing and Applications, 33, pp. 2929–2948 (2020).https://doi.org/10.1007/s00521-020-05434-0.
3. Ghasemiyeh, R., Moghdani, R., and Sana, S.S. “A hybrid artificial neural network with metaheuristic algorithms for predicting stock price”, Cybernetics and Systems, 48(4), pp. 365–392 (2017). https://doi.org/10.1080/01969722.2017.1285162.
4. Ospina-Mateus, H., Jiménez, L.A.Q., López-Valdés, F.J, et al. “Extraction of decision rules using genetic algorithms and simulated annealing for prediction of severity of traffic accidents by motorcyclists”, Journal of Ambient Intelligence and Humanized Computing, 12, pp. 10051–10072 (2021). https://doi.org/10.1007/s12652-020-02759-5.
5. Sarkar, B.K., Sana, S.S., and Chaudhuri, KA. “Genetic algorithm-based rule extraction system”, Applied Soft Computing Journal, 12(1), pp. 238–254 (2012).https://doi.org/10.1016/j.asoc.2011.08.049.
6. Brun, A. and Savino, M.M. “Assessing risk through composite FMEA with pairwise matrix and Markov chains”, International Journal of Quality and Reliability Management, 35(9), pp. 1709-1733 (2018). https://doi.org/10.1108/IJQRM-04-2017-0080.
7. Soltanali, H., Rohani, A., Tabasizadeh, M., et al. “An improved fuzzy inference system-based risk analysis approach with application to automotive production line”, Neural Computing and Applications, 32(14) (2020). https://doi.org/10.1007/s00521-019-04593-z.
8. Youssef, N.F. and Hyman, W.A. “A medical device complexity model: a new approach to medical equipment management”, Journal of Clinical Engineering, 34(2), pp. 94–98 (2009). https://doi.org/10.1097/JCE.0b013e31819fd711.
9. Taghipour, S., Banjevic, D., and Jardine, A.K.S.S. “Prioritization of medical equipment for maintenance decisions”, Journal of the Operational Research Society, 62(9), pp. 1666–1687 (2011). https://doi.org/10.1057/jors.2010.106.
10. Corciovă, C., Andritoi, D., Ciorap, R, et al. “Elements of risk assessment in medical equipment”, 2013, 8th International Symposium on Advanced Topics in Electrical Engineering (ATEE), pp. 1–4 (2013). https://doi.org/10.1109/ATEE.2013.6563427.
11. Tawfik, B., Ouda, B.K., and Abd El Samad, Y.M. “A fuzzy logic model for medical equipment risk classification”, Journal of Clinical Engineering, 38(4), pp 185–190, (2013). https://doi.org/10.1097/JCE.0b013e3182a90445.
12. Cheng, C.B., Shyur, H.J., and Kuo, Y.S. “Implementation of a flight operations risk assessment system and identification of critical risk factors”, Scientia Iranica, 21(6), pp. 2387–2398 (2014).
13. Onofrio, R., Piccagli, F., and Segato, F. “Failure mode, effects and criticality analysis (FMECA) for medical devices”, Procedia Manufacturing, 3(2), pp. 43–50 (2015). https://doi.org/10.1016/j.promfg.2015.07.106.
14. Jamshidi, A., Rahimi, S.A., Ait-Kadi, D., et al. “A comprehensive fuzzy risk-based maintenance framework for prioritization of medical devices”, Applied Soft Computing, 32, pp. 322–334 (2015). https://doi.org/10.1016/j.asoc.2015.03.054.
15. Kirkire, M.S., Rane, S.B., and Jadhav, J.R. “Risk management in medical product development process using traditional FMEA and fuzzy linguistic approach: A case study”, Journal of Industrial Engineering International, 11(4), pp. 595–611 (2015). https://doi.org/10.1007/s40092-015-0113-y.
16. Cicotti, G. and Coronato, A. “Towards a probabilistic model checking-based approach for medical device risk assessment”, 2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings, pp. 180– 185 (2015). https://doi.org/10.1109/MeMeA.2015.7145195.
17. Ardeshir, A., Mohajeri, M., and Amiri, M. “Evaluation of safety risks in construction using Fuzzy Failure Mode and Effect Analysis (FFMEA)”, Scientia Iranica, 23(6), pp. 2546–2556 (2016). https://doi.org/10.24200/sci.2016.2313.
18. Vazdani, S., Sabzghabaei, Gh., Dashti, S., et al. “FMEA techniques used in environmental risk assessment”, Environment and Ecosystem Science, 1(2), 16–18 (2017). https://doi.org/10.26480/ees.02.2017.16.18.
19. Lo, H.W. and Liou, J.J.H. “A novel multiple-criteria decision-making-based FMEA model for risk assessment”, Applied Soft Computing Journal, 73, pp. 684–696 (2018). https://doi.org/10.1016/j.asoc.2018.09.020.
20. Abdel-Basset, M., Manogaran, G., Gamal, A., et al. “A group decision making framework based on neutrosophic TOPSIS approach for smart medical device selection”, Journal of Medical Systems, 43(2), p. 38 (2019). https://doi.org/10.1007/s10916-019-1156-1.
21. Mangeli, M., Shahraki, A., and Saljooghi, F.H. “Improvement of risk assessment in the FMEA using nonlinear model, revised fuzzy TOPSIS, and support vector machine”, International Journal of Industrial Ergonomics, 69, pp. 209–216 (2019). https://doi.org/10.1016/j.ergon.2018.11.004.
22. Kim, D., Choi, J., and Han, K. “Risk managementbased security evaluation model for telemedicine systems”, BMC Medical Informatics and Decision Making, 20(1), pp. 1–14 (2020). https://doi.org/10.1186/s12911-020-01145-7.
23. Song, W., Li, J., Li, H., et al. “Human factors risk assessment: An integrated method for improving safety in clinical use of medical devices”, Applied Soft Computing Journal, 86 (2020). https://doi.org/10.1016/j.asoc.2019.105918.
24. Parand, F.A., Tavakoli-Golpaygani, A., and Rezvani, F. “Medical device risk assessment based on ordered weighted averaging aggregation operator”, Journal of Biomedical Physics and Engineering, 11(5), pp. 621-628 (2020). https://doi.org/10.31661/jbpe.v0i0.1133.
25. Ostadi, B. and Abbasi Harofteh, S. “A novel risk assessment approach using Monte Carlo simulation based on co-occurrence of risk factors: A case study of a petrochemical plant construction”,  Scientia Iranica, 29(3), pp. 1755-1765 (2020). https://doi.org/10.24200/sci.2020.55513.4258.
26. Subriadi, A.P. and Najwa, N.F. “The consistency analysis of Failure Mode and Effect Analysis (FMEA) in information technology risk assessment”, Heliyon, 6(1), pp. 31-61 (2020). https://doi.org/10.1016/j.heliyon.2020.e03161.
27. Moheimani, A., Sheikh, R., Hosseini, S.M.H., et al. “Assessing the agility of hospitals in disaster management: application of interval type-2 fuzzy Flowsort inference system”, Soft Computing,  5(5), pp. 3955–3974 (2021). https://doi.org/10.1007/s00500-020-05418-1.
28. Qin, J., Xi, Y., and Pedrycz, W. “Failure Mode and Effects Analysis (FMEA) for risk assessment based on interval type-2 fuzzy evidential reasoning method”, Applied Soft Computing Journal, 89, 106134 (2020). https://doi.org/10.1016/j.asoc.2020.106134.
29. Bhattacharjee, P., Dey, V., and Mandal, U.K. “Risk assessment by Failure Mode and Effects Analysis (FMEA) using an interval number based logistic regression model”, Safety Science, 132, 104967 (2020). https://doi.org/10.1016/j.ssci.2020.104967.
30. Martinez-Licona, F.M. and Perez-Ramos, S.E. “A risk assessment method based on the failure analysis of medical devices in the adult intensive care unit”, Global Clinical Engineering Journal, 4(2), pp. 15– 25 (2021). https://doi.org/10.31354/globalce.v4i2.124.
31. Chen, J. and Wang, D. “Government credit risk assessment of non-profit public-private partnership projects in China based on the IVHFSs-IFAHP model”, Scientia Iranica, 28(1), pp. 38–48 (2021). https://doi.org/10.24200/sci.2018.50561.1763.
32. Lin, C.-J. and Wu, W.-W. “A causal analytical method for group decision-making under fuzzy environment”, Expert Systems with Applications, 34(1), pp. 205–213 (2008). https://doi.org/10.1016/j.eswa.2006.08.012.
33. Wong, K.K.-K. “Partial Least Squares Structural Equation Modeling (PLS-SEM) techniques using smart PLS”, Marketing Bulletin, 24(1), pp. 1–32 (2013). 
34. Birjandi, A.K., Akhyani, F., Sheikh, R., et al. “Evaluation and selecting the contractor in bidding with incomplete information using MCGDM method”, Soft Computing, 23(20), pp. 10569–10585 (2019). https://doi.org/10.1007/s00500-019-04050-y.
35. Baykasoğlu, A. and Gölcük, İ. “Development of an interval type-2 fuzzy sets based hierarchical MADM model by combining DEMATEL and TOPSIS”, Expert Systems with Applications, 70, pp. 37–51 (2017). https://doi.org/10.1016/j.eswa.2016.11.001.
36. Lin, K.-P.P., Tseng, M.-L.L., and Pai, P.-F.F. “Sustainable supply chain management using approximate fuzzy DEMATEL method. Resources”, Conservation and Recycling, 128, pp. 134–142 (2018). https://doi.org/10.1016/j.resconrec.2016.11.017.
37. Haseli, G., Sheikh, R., and Sana, S.S. “Basecriterion on multi-criteria decision-making method and its applications”, International Journal of Management Science and Engineering Management, 15(2), pp. 79–88 (2020). https://doi.org/10.1080/17509653.2019.1633964.
38. Guo, S. and Zhao, H. “Fuzzy best-worst multicriteria decision-making method and its applications”, Knowledge-Based Systems, 121, pp. 23–31 (2017). https://doi.org/10.1016/j.knosys.2017.01.010.
39. Hadi-Vencheh, A., Hejazi, S., and Eslaminasab, Z. “A fuzzy linear programming model for risk evaluation in failure mode and effects analysis”, Neural Computing and Applications, 22(6), pp. 1105–1113 (2013). https://doi.org/10.1007/s00521-012-0874-9.
40. Jamali, G., Sana, S.S., and Moghdani, R. “Hybrid improved cuckoo search algorithm and genetic algorithm for solving Markov-modulated demand”, RAIRO-Operations Research, 52(2), pp. 473–497. (2018). https://doi.org/10.1051/ro/2017076.
41. Xiao, N., Huang, H.-Z.Z., Li, Y., et al. “Multiple failure modes analysis and weighted risk priority number evaluation in FMEA”, Engineering Failure Analysis, 18(4), pp. 1162–1170 (2011). https://doi.org/10.1016/j.engfailanal.2011.02.004.
42. Rhee, S.J. and Ishii, K. “Life cost-based FMEA using empirical data”, Proceedings of the ASME Design Engineering Technical Conference, 3, pp. 167–175 (2003). https://doi.org/10.1115/DETC2003/DFM-48150.
43. Geum, Y., Cho, Y., and Park, Y. “A systematic approach for diagnosing service failure: Servicespecific FMEA and grey relational analysis approach”, Mathematical and Computer Modelling, 54(11–12), pp. 3126–3142 (2011). https://doi.org/10.1016/j.mcm.2011.07.042.
44. Sharma, R.K., Kumar, D., and Kumar, P. “Systematic Failure Mode and Effect Analysis (FMEA) using fuzzy linguistic modelling”, International Journal of Quality and Reliability Management, 22(9), pp. 986–1004 (2005). https://doi.org/10.1108/02656710510625248?urlap pend=%3Futm_source%3Dresearchgate.
45. DeRosier, J., Stalhandske, E., Bagian, J.P., et al. “Using health care failure mode and effect analysisTM: The VA national center for patient safety’s prospective risk analysis system”, The  point Commission Journal on Quality Improvement, 28(5), pp. 248–267 (2002). https://doi.org/10.1016/s1070-3241(02)28025-6.
46. Wang, B., Medical Equipment Maintenance: Management and Oversight, Synthesis Lectures on Biomedical Engineering, 7(2), pp. 1–85 (2012). https://doi.org/10.1007/978-3-031-01655-4.
Volume 32, Issue 8
Transactions on Industrial Engineering
March and April 2025 Article ID:5266
  • Receive Date: 08 January 2021
  • Revise Date: 28 September 2021
  • Accept Date: 25 April 2022