Volume 25, Issue 4 (3-2023)
jha 2023, 25(4): 104-124 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Ghaderi F, Rajabzadeh Ghatari A, Radfar R. An intelligent decision support system based on fuzzy techniques and neural networks for purchasing medical supplies. jha 2023; 25 (4) :104-124
URL: http://jha.iums.ac.ir/article-1-4219-en.html
URL: http://jha.iums.ac.ir/article-1-4219-en.html
1- Ph.D Candidate, Department of Information Technology Management, Science and Research Branch, Islamic Azad University, Tehran
2- professor, Department of Industrial Management, Tarbiat Modares University, Tehran ,alirajabzadeh@modares.ac.ir
3- Professor, Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran
2- professor, Department of Industrial Management, Tarbiat Modares University, Tehran ,
3- Professor, Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran
Abstract: (1119 Views)
Introduction: The supply chains of medical equipment and necessities in healthcare centers are highly complex, diverse, and dynamic, making optimal selection and purchase a specialized and challenging task. This research aimed to design an intelligent decision support system that could aid the expertise process of purchasing medical supplies.
Methods: In this developmental-applied and descriptive-survey study, we used artificial intelligence, fuzzy sets, and neural networks as well as MATLAB software to design a model that could simulate the decision-making process of experts in the purchase of medical supplies by predicting the score of medical supplies after obtaining information. The required data was extracted from the website of the General Directorate of Medical Equipment and Knowledge of Specialists in 2022.
Results: The results showed that a three-layer perceptron neural network, with a mean square error of 0.0011 and an overall correlation of 0.97, could be used as a suitable decision aid in the evaluation and selection of medical supplies.
Conclusion: The use of an intelligent decision support system can greatly aid the expertise process of purchasing medical supplies, thus helping the healthcare system to preserve resources and improve the quality of healthcare services. The high speed and accuracy of processing data by using neural networks, one of the most effective methods for learning and generalizing, can assist experts in purchasing medical supplies and lead to optimal decision-making.
Methods: In this developmental-applied and descriptive-survey study, we used artificial intelligence, fuzzy sets, and neural networks as well as MATLAB software to design a model that could simulate the decision-making process of experts in the purchase of medical supplies by predicting the score of medical supplies after obtaining information. The required data was extracted from the website of the General Directorate of Medical Equipment and Knowledge of Specialists in 2022.
Results: The results showed that a three-layer perceptron neural network, with a mean square error of 0.0011 and an overall correlation of 0.97, could be used as a suitable decision aid in the evaluation and selection of medical supplies.
Conclusion: The use of an intelligent decision support system can greatly aid the expertise process of purchasing medical supplies, thus helping the healthcare system to preserve resources and improve the quality of healthcare services. The high speed and accuracy of processing data by using neural networks, one of the most effective methods for learning and generalizing, can assist experts in purchasing medical supplies and lead to optimal decision-making.
Keywords: Intelligent Decision Support System, purchasing expertise, medical supplies, multi-layer perceptron network, fuzzy sets
Type of Study: Research |
Subject:
Health Information Management
Received: 2022/09/29 | Accepted: 2022/12/21 | Published: 2023/05/20
Received: 2022/09/29 | Accepted: 2022/12/21 | Published: 2023/05/20
References
1. Lari A, Komeili A, Hajinabi K, Riahi L. Designing a model of medical equipment purchase management in hospitals of Tehran University of Medical Sciences. Asia Pacific Journal of Health Management. 2021;16(2):137-47. [DOI:10.24083/apjhm.v16i2.745]
2. Ghaderi F, Moradhasel B. Resources and expenditures management in the field of medical supplies using K-means clustering algorithm: A case study. Journal of Health Administration. 2021;23(4):51-60. [In Persian]. [DOI:10.29252/jha.23.4.51]
3. Zhang Y, Zhou Z, Si Y. When more is less: What explains the overuse of health care services in China? Soc Sci Med. 2019;232:17-24. [DOI:10.1016/j.socscimed.2019.04.018]
4. Tiryakioglu M, Yulek MA. Development-based public procurement policies: A selective survey of literature, cross-country policy experience and the Turkish experience. Innovat Eur J Soc Sci Res. 2015;28(3):344-59. [DOI:10.1080/13511610.2015.1056723]
5. Baral MM, Chittipaka V, Mukherjee S, Rao P. Medicine procurement in the healthcare sector: An intelligent supply chain perspective. Journal of critical reviews. 2020;7(7):1812-28.
6. Hani U, Basri MH, Winarso D. Inventory management of medical consumables in public hospital: A case study. Management. 2013;3(2):128-33.
7. Firouzi Jahan Tigh F, Dehghani S. Presenting a model in quality management of hospital medical equipment supply chain using game theory. Scientific Journal of Supply Chain Management. 2016;17(50):68-78. [In Persian]
8. Davis P. A review of procurement practices in Ireland of medical devices. Ireland: IMSTA; 2012. 29 p.
9. Yang C, Wang Y, Hu X, Chen Y. Qian L, Li F, et al. Improving hospital based medical procurement decisions with health technology assessment and multi-criteria decision analysis. Inquiry. 2021;58:1 -13. [DOI:10.1177/00469580211022911]
10. Vecchi V, Cusumano N, Boyer EJ. Medical supply acquisition in Italy and the United States in the Era of COVID-19: The case for strategic procurement and public-private partnerships. Am Rev Publ Admin. 2020;50(6-7): 642-9. [DOI:10.1177/0275074020942061]
11. Chakravaddi I. Medical equipment industry in India: Production, procurement and utilization. Indian J Public Health. 2013;57(4):203-7.
12. Kohler JC, Wright T. The urgent need for transparent and accountable procurement of medicine and medical supplies in times of COVID-19 pandemic. J Pharm Policy Pract. 2020;13:1-4. [DOI:10.1186/s40545-020-00256-w]
13. Ellahi H, Kamaei A, Safaei A, Sharafi Rad E. The role of good logistics in providing medical supplies in order to protect the right to health through the medical community. Medical Law Journal. 2021;15(56):365-81. [In Persian]
14. Abdi Talarposhti M, Mahmodi Gh, Jahani M. Factors affecting supply chain agility at hospitals in Iran. Journal of Health Administration 2016;19(64):7-18. [In Persian]
15. Lari A, Hajinabi K, Komeili A, Riahi L. The effective factors in the control component of purchase management of medical consumables. Iranian Journal of Nursing Research. 2019;14(4):66-71. [In Persian]
16. Jafarnejad A, Soleymani M. Demand forecasting medical equipment based on artificial neural networks and ARIMA methods. Journal of Economic Research and Policies. 2011;19(57):171-98. [In Persian]
17. Heydarian N, Vahdat Sh. The impact of implantation of health care reform plan in patients pay out of pocket in selected public hospitals in Isfahan. Journal of Medical Council of Iran. 2015;33(3):187-94. [In Persian]
18. Phillips-Wren G. AI tools in decision making support systems: A review. Int J Artif Intell Tools. 2012;21(2):1-13. [DOI:10.1142/S0218213012400052]
19. Wen W, Wang WK, Wang CH. A knowledge-based intelligent decision support system for national defense budget planning. Expert Syst Appl. 2005;28(1):55-66. [DOI:10.1016/j.eswa.2004.08.010]
20. Hafez Nia M. An introduction to the research method in humanities. 14th ed. Samt Publications, 2008. [In Persian]
21. Golmohammadi D. Neural network application for fuzzy multi-criteria decision making problems. Int J Prod Econ. 2011;131(2):490-504. [DOI:10.1016/j.ijpe.2011.01.015]
22. Gegovska T, Koker R, Cakar T. Green supplier selection using fuzzy multiple-criteria decision-making methods and artificial neural networks. Comput Intell Neurosci. 2020;2020:1-26. [DOI:10.1155/2020/8811834]
23. Chen S-J, Hwang C-L. Fuzzy multiple attribute decision making: Methods and applications. In: Dawid H, Dimitrov D, Gerber A, Haake C-J, Hofmann C, Pfeiffer T, et al, editors. Lecture Notes in Economics and Mathematical Systems (LNE). Switzerland: Springer Verlag Publication. 1991. (LNE, volume 375). [DOI:10.1007/978-3-642-46768-4_5]
24. Abdos M, Mozayani N. Solving multi-criteria decision making problems using artificial neural networks. Iranian Journal of Electrical and Computer Engineering. 2007;5(1):47-52. [In Persian]
25. Asgharpour MJ. Multi-criteria decision making. 3th ed. Tehran: Tehran University, 2004. [In Persian]
26. Jain YK, Bhandare SK. Min max normalization based data perturbation method for privacy protection. International Journal of Computer and Communication Technology. 2013;4(4):233-8. [DOI:10.47893/IJCCT.2013.1201]
27. Hill T, O'Connor M, Remus W. Neural network models for time series forecasts. Manage Sci. 1996;42(7):1082-92. [DOI:10.1287/mnsc.42.7.1082]
28. Fallahnia M. Estimating energy consumption of educational spaces using artificial neural networks. Hoviat Shahr. 2018;11(32):17-30. [In Persian]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
Site Keywords
Health, Administration, Scientific Article, Iran University of Medical Sciences,
