A New Extended Approach to Reduce Admission Time in Hospital Operating Rooms Based on the FMEA Method in an Uncertain Environment
DOI:
https://doi.org/10.31181/jscda11202310Keywords:
Failure mode and effects analysis, Operation room, Z-number theory, additive ratio assessment, Best Worst methodAbstract
Operating rooms (ORs) are one of the essential hospital resources and optimal management can result in efficient OR usage. The admission time reduction before surgery in the ORs can lead to on-time surgery and efficient use of ORs. This study addresses this issue by identifying the main failure modes that cause delays in ORs. The conventional methodology known as Failure Mode and Effects Analysis (FMEA) represents one of the prevailing techniques utilized for the purpose of ascertaining failure modes within a given process. This involves the assignment of numerical scores to each failure mode, with the intention of utilizing the resultant Risk Priority Number (RPN) to facilitate the identification of said failure modes. However, RPN scoring has been criticized for some deficiencies. This study proposes a three-phase approach to address some of the shortcomings of the FMEA method. The initial stage involves utilizing the FMEA approach to recognize failure modes and assess the crucial elements of RPN. Following this, the second stage employs the Z-BWM technique and expert insights to determine the weights of the five essential factors. Lastly, in the third phase, risks are prioritized using the proposed Z-ARAS method based on the outputs of the previous phases. This approach considers the uncertainty in the determining factors and assigns different weights to them, while also taking into account the reliability of the risks through the Z-Number theory. Finally, comparing the proposed approach with other traditional approaches, reinforces the usefulness of the proposed method in evaluating failure modes in OR management.
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Lee, A. R., Cho, Y., Jin, S., & Kim, N. (2020). Enhancement of surgical hand gesture recognition using a capsule network for a contactless interface in the operating room. Computer Methods and Programs in Biomedicine, 190, 105385. https://doi.org/10.1016/j.cmpb.2020.105385.
Dexter, F., & Macario, A. (2004). When to release allocated operating room time to increase operating room efficiency. Anesthesia & Analgesia, 98(3), 758-762. https://doi.org/10.1213/01.ANE.0000100739.03919.26.
Fong, A. J., Smith, M., & Langerman, A. (2016). Efficiency improvement in the operating room. Journal of Surgical Research, 204(2), 371-383. https://doi.org/10.1016/j.jss.2016.04.054.
Kumar, M., & Malhotra, S. (2017). Reasons for delay in turnover time in operating room-an observational study. Bangladesh journal of medical science, 16(2). https://doi.org/10.3329/bjms.v16i2.27473.
Ceschia, S., & Schaerf, A. (2016). Dynamic patient admission scheduling with operating room constraints, flexible horizons, and patient delays. Journal of Scheduling, 19, 377-389. https://doi.org/10.1007/s10951-014-0407-8.
Redmann, A. J., Robinette, K., Myer, C. M., de Alarcón, A., Veid, A., & Hart, C. K. (2018). Association of Reduced Delay in Care With a Dedicated Operating Room in Pediatric Otolaryngology. JAMA Otolaryngology–Head & Neck Surgery, 144(4), 330-334. https://doi.org/10.1001/jamaoto.2017.3165.
Zhu, S., Fan, W., Liu, T., Yang, S., & Pardalos, P. M. (2020). Dynamic three-stage operating room scheduling considering patient waiting time and surgical overtime costs. Journal of Combinatorial Optimization, 39, 185-215. https://doi.org/10.1007/s10878-019-00463-5.
Garbey, M., Joerger, G., Huang, A., Salmon, R., Kim, J., Sherman, V., ... & Bass, B. (2015). An intelligent hospital operating room to improve patient health care. Journal of Computational Surgery, 2(1), 1-10. https://doi.org/10.1186/s40244-015-0016-7.
Barbagallo, S., Corradi, L., De Ville de Goyet, J., Iannucci, M., Porro, I., Rosso, N., ... & Testi, A. (2015). Optimization and planning of operating theatre activities: an original definition of pathways and process modeling. BMC medical informatics and decision making, 15, 1-16. https://doi.org/10.1186/s12911-015-0161-7.
Wang, Y., Zhang, Y., & Tang, J. (2019). A distributionally robust optimization approach for surgery block allocation. European Journal of Operational Research, 273(2), 740-753. https://doi.org/10.1016/j.ejor.2018.08.037.
Zachwieja, E., Yayac, M., Wills, B. W., Wilt, Z., Austin, M. S., & Courtney, P. M. (2020). Overlapping surgery increases operating room efficiency without adversely affecting outcomes in total hip and knee arthroplasty. The Journal of arthroplasty, 35(6), 1529-1533. https://doi.org/10.1016/j.arth.2020.01.062.
Roshanaei, V., Luong, C., Aleman, D. M., & Urbach, D. (2017). Propagating logic-based Benders’ decomposition approaches for distributed operating room scheduling. European Journal of Operational Research, 257(2), 439-455. https://doi.org/10.1016/j.ejor.2016.08.024.
Hamid, M., Nasiri, M. M., Werner, F., Sheikhahmadi, F., & Zhalechian, M. (2019). Operating room scheduling by considering the decision-making styles of surgical team members: a comprehensive approach. Computers & Operations Research, 108, 166-181. https://doi.org/10.1016/j.cor.2019.04.010.
Rivera, G., Cisneros, L., Sánchez-Solís, P., Rangel-Valdez, N., & Rodas-Osollo, J. (2020). Genetic algorithm for scheduling optimization considering heterogeneous containers: A real-world case study. Axioms, 9(1), 27. https://doi.org/10.3390/axioms9010027.
Shultz, J., Borkenhagen, D., Rose, E., Gribbons, B., Rusak-Gillrie, H., Fleck, S., ... & Filer, J. (2020). Simulation-based mock-up evaluation of a universal operating room. HERD: Health Environments Research & Design Journal, 13(1), 68-80. https://doi.org/10.1177/193758671985577.
Nazarian-Jashnabadi, J., Bonab, S. R., Haseli, G., Tomaskova, H., & Hajiaghaei-Keshteli, M. (2023). A dynamic expert system to increase patient satisfaction with an integrated approach of system dynamics, ISM, and ANP methods. Expert Systems with Applications, 234, 121010. https://doi.org/10.1016/j.eswa.2023.121010.
King, Z., Farrington, J., Utley, M., Kung, E., Elkhodair, S., Harris, S., ... & Crowe, S. (2022). Machine learning for real-time aggregated prediction of hospital admission for emergency patients. NPJ Digital Medicine, 5(1), 104. https://doi.org/10.1038/s41746-022-00649-y.
Dodaro, C., Galatà, G., Maratea, M., & Porro, I. (2019). An ASP-based framework for operating room scheduling. Intelligenza Artificiale, 13(1), 63-77. https://doi.org/10.3233/IA-190020.
Tagge, E. P., Thirumoorthi, A. S., Lenart, J., Garberoglio, C., & Mitchell, K. W. (2017). Improving operating room efficiency in academic children's hospital using Lean Six Sigma methodology. Journal of pediatric surgery, 52(6), 1040-1044. https://doi.org/10.1016/j.jpedsurg.2017.03.035.
Fleischmann, R., Jairath, V., Mysler, E., Nicholls, D., & Declerck, P. (2020). Nonmedical switching from originators to biosimilars: does the nocebo effect explain treatment failures and adverse events in rheumatology and gastroenterology?. Rheumatology and Therapy, 7, 35-64. https://doi.org/10.1007/s40744-019-00190-7.
Wong, J., Khu, K. J., Kaderali, Z., & Bernstein, M. (2010). Delays in the operating room: signs of an imperfect system. Canadian Journal of surgery, 53(3), 189.
Qazi, A., Quigley, J., Dickson, A., & Ekici, Ş. Ö. (2017). Exploring dependency based probabilistic supply chain risk measures for prioritising interdependent risks and strategies. European Journal of Operational Research, 259(1), 189-204. https://doi.org/10.1016/j.ejor.2016.10.023.
Qin, J., Xi, Y., & Pedrycz, W. (2020). Failure mode and effects analysis (FMEA) for risk assessment based on interval type-2 fuzzy evidential reasoning method. Applied Soft Computing, 89, 106134. https://doi.org/10.1016/j.asoc.2020.106134.
Chen, Y., Ran, Y., Wang, Z., Li, X., Yang, X., & Zhang, G. (2020). An extended MULTIMOORA method based on OWGA operator and Choquet integral for risk prioritization identification of failure modes. Engineering Applications of Artificial Intelligence, 91, 103605. https://doi.org/10.1016/j.engappai.2020.103605.
Liu, H. C., & Liu, H. C. (2019). FMEA for proactive healthcare risk analysis: a systematic literature review. Improved FMEA methods for proactive healthcare risk analysis, 15-45. https://doi.org/10.1007/978-981-13-6366-5_2.
Rah, J. E., Manger, R. P., Yock, A. D., & Kim, G. Y. (2016). A comparison of two prospective risk analysis methods: Traditional FMEA and a modified healthcare FMEA. Medical Physics, 43(12), 6347-6353. https://doi.org/10.1118/1.4966129.
Ghoushchi, S. J., Bonab, S. R., Ghiaci, A. M., Haseli, G., Tomaskova, H., & Hajiaghaei-Keshteli, M. (2021). Landfill site selection for medical waste using an integrated SWARA-WASPAS framework based on spherical fuzzy set. Sustainability, 13(24), 13950. https://doi.org/10.3390/su132413950.
Ouyang, L., Zhu, Y., Zheng, W., & Yan, L. (2021). An information fusion FMEA method to assess the risk of healthcare waste. Journal of Management Science and Engineering, 6(1), 111-124. https://doi.org/10.1016/j.jmse.2021.01.001.
Jafarzadeh Ghoushchi, S., Memarpour Ghiaci, A., Rahnamay Bonab, S., & Ranjbarzadeh, R. (2022). Barriers to circular economy implementation in designing of sustainable medical waste management systems using a new extended decision-making and FMEA models. Environmental Science and Pollution Research, 29(53), 79735-79753. https://doi.org/10.1007/s11356-022-19018-z.
Ben‐Daya, M., & Raouf, A. (1996). A revised failure mode and effects analysis model. International Journal of Quality & Reliability Management, 13(1), 43-47. https://doi.org/10.1108/02656719610108297.
Ghoushchi, S. J., Yousefi, S., & Khazaeili, M. (2019). An extended FMEA approach based on the Z-MOORA and fuzzy BWM for prioritization of failures. Applied soft computing, 81, 105505. https://doi.org/10.1016/j.asoc.2019.105505.
Zavadskas, E. K., Turskis, Z., & Kildienė, S. (2014). State of art surveys of overviews on MCDM/MADM methods. Technological and economic development of economy, 20(1), 165-179. https://doi.org/10.3846/20294913.2014.892037.
Rahnamay Bonab, S., & Osgooei, E. (2022). Environment risk assessment of wastewater treatment using FMEA method based on Pythagorean fuzzy multiple-criteria decision-making. Environment, Development and Sustainability, 1-31. https://doi.org/10.1007/s10668-022-02555-5.
Haseli, G., Ranjbarzadeh, R., Hajiaghaei-Keshteli, M., Ghoushchi, S. J., Hasani, A., Deveci, M., & Ding, W. (2023). HECON: Weight assessment of the product loyalty criteria considering the customer decision's halo effect using the convolutional neural networks. Information Sciences, 623, 184-205. https://doi.org/10.1016/j.ins.2022.12.027.
Bonab, S. R., Ghoushchi, S. J., Deveci, M., & Haseli, G. (2023). Logistic autonomous vehicles assessment using decision support model under spherical fuzzy set integrated Choquet Integral approach. Expert Systems with Applications, 214, 119205. https://doi.org/10.1016/j.eswa.2022.119205.
Moons, K., Waeyenbergh, G., Pintelon, L., Timmermans, P., & De Ridder, D. (2019). Performance indicator selection for operating room supply chains: An application of ANP. Operations Research for Health Care, 23, 100229. https://doi.org/10.1016/j.orhc.2019.100229.
Momen, S., Tavakkoli-Moghaddam, R., Ghasemkhani, A., Shahnejat-Bushehri, S., & Tavakkoli-Moghaddam, H. (2019). Prioritizing surgical cancellation factors based on a fuzzy best-worst method: a case study. IFAC-PapersOnLine, 52(13), 112-117. https://doi.org/10.1016/j.ifacol.2019.11.161.
Hamid, M., Hamid, M., Nasiri, M. M., & Ebrahimnia, M. (2018). Improvement of operating room performance using a multi-objective mathematical model and data envelopment analysis: A case study. International journal of industrial engineering & production research, 29(2), 117-132. https://doi.org/10.22068/ijiepr.29.2.117.
Cappanera, P., Visintin, F., & Banditori, C. (2018). Addressing conflicting stakeholders’ priorities in surgical scheduling by goal programming. Flexible Services and Manufacturing Journal, 30, 252-271. https://doi.org/10.1007/s10696-016-9255-5.
Li, X., Rafaliya, N., Baki, M. F., & Chaouch, B. A. (2017). Scheduling elective surgeries: the tradeoff among bed capacity, waiting patients and operating room utilization using goal programming. Health care management science, 20, 33-54. https://doi.org/10.1007/s10729-015-9334-2.
O’Neill, L., & Dexter, F. (2007). Tactical increases in operating room block time based on financial data and market growth estimates from data envelopment analysis. Anesthesia & Analgesia, 104(2), 355-368. https://doi.org/10.1213/01.ane.0000253092.04322.23.
Ozkarahan, I. (2000). Allocation of surgeries to operating rooms by goal programing. Journal of Medical Systems, 24, 339-378. https://doi.org/10.1023/A:1005548727003.
Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57. https://doi.org/10.1016/j.omega.2014.11.009.
Talib, F., Asjad, M., Attri, R., Siddiquee, A. N., & Khan, Z. A. (2019). Ranking model of total quality management enablers in healthcare establishments using the best-worst method. The TQM Journal, 31(5), 790-814. https://doi.org/10.1108/TQM-04-2019-0118.
Luo, Y., Chen, X., & Sun, Y. (2019, April). A Fuzzy linguistic method for evaluating doctors of online healthcare consultation platform using BWM and prospect theory. In 2019 IEEE 6th International Conference on Industrial Engineering and Applications (ICIEA) (pp. 506-510). IEEE. https://doi.org/10.1109/IEA.2019.8715035.
Bonab, S. R., Haseli, G., Rajabzadeh, H., Ghoushchi, S. J., Hajiaghaei-Keshteli, M., & Tomaskova, H. (2023). Sustainable resilient supplier selection for IoT implementation based on the integrated BWM and TRUST under spherical fuzzy sets. Decision making: applications in management and engineering, 6(1), 153-185. https://doi.org/10.31181/dmame12012023b.
Haseli, G., Torkayesh, A. E., Hajiaghaei-Keshteli, M., & Venghaus, S. (2023). Sustainable resilient recycling partner selection for urban waste management: Consolidating perspectives of decision-makers and experts. Applied Soft Computing, 137, 110120. https://doi.org/10.1016/j.asoc.2023.110120.
Babroudi, N. E. P., Sabri-Laghaie, K., & Ghoushchi, N. G. (2021). Re-evaluation of the healthcare service quality criteria for the Covid-19 pandemic: Z-number fuzzy cognitive map. Applied Soft Computing, 112, 107775. https://doi.org/10.1016/j.asoc.2021.107775.
Zadeh, L. A. (2011). A note on Z-numbers. Information sciences, 181(14), 2923-2932. https://doi.org/10.1016/j.ins.2011.02.022.
Zavadskas, E. K., & Turskis, Z. (2010). A new additive ratio assessment (ARAS) method in multicriteria decision‐making. Technological and economic development of economy, 16(2), 159-172. https://doi.org/10.3846/tede.2010.10
Balezentiene, L., & Kusta, A. (2012). Reducing greenhouse gas emissions in grassland ecosystems of the central Lithuania: multi-criteria evaluation on a basis of the ARAS method. The Scientific World Journal, 2012. https://doi.org/10.1100/2012/908384.
Sen, H. (2017). Hospital location selection with ARAS-G. The Eurasia Proceedings of Science, Technology, Engineering & Mathematics, 1, 359-365. http://www.epstem.net/en/pub/issue/31865/365040
Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X.
Ghoushchi, S. J., Osgooei, E., Haseli, G., & Tomaskova, H. (2021). A novel approach to solve fully fuzzy linear programming problems with modified triangular fuzzy numbers. Mathematics, 9(22), 2937. https://doi.org/10.3390/math9222937.
Zafaranlouei, N., Ghoushchi, S. J., & Haseli, G. (2023). Assessment of sustainable waste management alternatives using the extensions of the base criterion method and combined compromise solution based on the fuzzy Z-numbers. Environmental Science and Pollution Research, 30(22), 62121-62136. https://doi.org/10.1007/s11356-023-26380-z.
Haseli, G., Sheikh, R., & Sana, S. S. (2020). Base-criterion on multi-criteria decision-making method and its applications. International journal of management science and engineering management, 15(2), 79-88. https://doi.org/10.1080/17509653.2019.1633964.
Haseli, G., & Sheikh, R. (2022). Base criterion method (BCM). In Multiple Criteria Decision Making: Techniques, Analysis and Applications (pp. 17-38). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-16-7414-3_2.
Haseli, G., & Jafarzadeh Ghoushchi, S. (2022). Extended base-criterion method based on the spherical fuzzy sets to evaluate waste management. Soft Computing, 26(19), 9979-9992. https://doi.org/10.1007/s00500-022-07366-4.
Ahmadi, H. B., Kusi-Sarpong, S., & Rezaei, J. (2017). Assessing the social sustainability of supply chains using Best Worst Method. Resources, Conservation and Recycling, 126, 99-106. https://doi.org/10.1016/j.resconrec.2017.07.020.
Guo, S., & Zhao, H. (2017). Fuzzy best-worst multi-criteria decision-making method and its applications. Knowledge-Based Systems, 121, 23-31. https://doi.org/10.1016/j.knosys.2017.01.010.
Aboutorab, H., Saberi, M., Asadabadi, M. R., Hussain, O., & Chang, E. (2018). ZBWM: The Z-number extension of Best Worst Method and its application for supplier development. Expert Systems with Applications, 107, 115-125. https://doi.org/10.1016/j.eswa.2018.04.015.
Heidary Dahooie, J., Kazimieras Zavadskas, E., Abolhasani, M., Vanaki, A., & Turskis, Z. (2018). A novel approach for evaluation of projects using an interval–valued fuzzy additive ratio assessment (ARAS) method: a case study of oil and gas well drilling projects. Symmetry, 10(2), 45. https://doi.org/10.3390/sym10020045.
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