Çocuk Yoğun Bakım Ünitesinde Çok Kriterli Karar Verme ile Mekanik Ventilatör Seçimi ve Bir Uygulama Örneği

Yoğun bakım üniteleri, hayati önem arz eden tıbbi tedavi birimlerinden biridir. Bu ünitede yapılacak en küçük hata veya gecikme, telafisi olmayan sonuçlar doğurabilir. Yoğun bakımlarda başvurulan en önemli cihazlardan birisi, hastaya yapay solunum desteği veren mekanik ventilatörlerdir. Ventilatör seçimleri ve parametre ayarları, hastanın yaşamını devam ettirmesinde en önemli faktörlerdir. Bu nedenle, ventilatör seçiminin en doğru şekilde gerçekleştirilmesi kritik önem arz eder. Bu çalışmada, Adana ilindeki bir hastanenin çocuk yoğun bakım ünitesinde ventilatör seçimi için çok kriterli karar verme tekniklerinden Analitik Hiyerarşi Prosesi (AHP) ve bulanık TOPSIS yöntemleri kullanılmıştır. Her iki yöntem neticesinde çıkan sonuçlar karşılaştırılmıştır.

Selection of Mechanical Ventilator in Pediatric Intensive Care Unit by MultiCriteria Decision Making and a Case Study

Intensive care units are one of the medical treatment units which are of vital importance. The minimum fault or delay in this unit can cause nonrecoverable results. One of the most important tools applied in intensive cares are mechanic ventilators which supports artificial respiration to the patient. Appropriate ventilator selections and ventilatory parameters are the most significant factors for patient’s survival. Therefore, carrying out the ventilator selection most correctly is critical. In this study, Analytic Hierarcy Process (AHP) and fuzzy TOPSIS methods which are multi criteria decision making techniques are utilized for selection of ventilator in a child intensive care unit of a hospital in Adana county. The results obtained from both methods are compared.

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Torpy, J.M., Campbell, A.D., Glass, R.M., 2010. Mechanical Ventilation, Journal of American Medical Association, 902.
Kacmarek, R.M., 2011. The Mechanical Ventilator: Past, Present, and Future, Respiratory Care, 1170-1180.
Perel, A, Stock, M.C., 1992. Handbook of Mechanical Ventilatory Support (First Edition), Williams and Wilkins, Philadelphia, 7.
Kacmarek, R.M., Venegas, J., 1987. Mechanical Ventilatory Rates and Tidal Volumes. Respiratory Care, 32, 466.
Abou-Shala, N., Meduri, U., 1996. Noninvasive Mechanical Ventilation in Patients with Acute Respiratory Failure, Critical Care Medicine, 24, 705–715.
Mehta, S., Hill N.S., 2001. Noninvasive Ventilation. American Journal of Respiratory Critical Care Medicine, 163, 540–577.
Nava, S., Hill, N., 2013. Non-Invasive Ventilation in Acute Respiratory Failure, Lancet, 374, 250-259.
Brochard, L., 2003. Mechanical Ventilation: Invasive Versus Noninvasive, European Respiratory Journal, 47, 31-37.
Sloane, E.B., Liberatore, M. J., Nydick, R.L., Luo, W., Chung, Q.B., 2003. Using the Analytic Hierarchy Process as a Clinical Engineering Tool to Facilitate an Iterative, Multidisciplinary, Microeconomic Health Technology Assessment, Computers & Operations Research, 30(10), 1447-1465.
Diaby, V., Campbell, K., Goeree, R., 2013. Multi-Criteria Decision Analysis (MCDA) in Health Care: A Bibliometric Analysis, Operations Research for Health Care, 2(1-2), 20-24.
Karacan, I., Tozan, H., Karatas, M., 2016. Multi-Criteria Decision Methods in Helath Technology Assessment: A Brief Literature Review, Eurasian Journal of Health Technology Assessment, 1(1), 12-19.
Liberatore, M.J., Nydick, R.L., 2008. The Analytic Hierarchy Process in Medical and Health Care Decision Making: A Literature Review, European Journal of Operations Research, 189(1), 194-207.
Schmidt, K., Aumann, I., Hollander, I., Damm, K., von der Schulenburg, J.M.G., 2015. Applying the Analytic Hierarchy Process in Healthcare Research: A Systematic Literature Review and Evaluation of Reporting, BMC Medical Informatics and Decision Making, 15(112), 27.
Dolan, J.G., Isseslhardt, B.J., Cappuccio, J.D., 1989. The Analytic Hierarchy Process in Medical Decision Making: A Tutorial, 9(1), 40-50.
Suner, A., Celikoglu, C.C., Dicle, O., Sokmen, S., 2012. Sequential Decision Tree Using the Analytical Hierarchy Process for Decision Support in Rectal Cancer, Artificial Intelligence in Medicine, 56(1), 59-68.
Aktas, A., Cebi, S., Temiz, I., 2015. A New Evaluation Model for Service Quality of Health Care Systems Based on AHP and Information Axiom, Journal of Intelligent & Fuzzy Systems, 28(3), 1009-1021.
Liu, H.C., Wu, J., Li, P., 2013. Assessment of Health-Care Waste Disposal Methods Using a VIKOR-Based Fuzzy Multi-Criteria Decision Making Method, 33(12), 2744-2751.
Chang, T.H., 2014. Fuzzy VIKOR Method: A Case Study of the Hospital Service Evaluation in Taiwan, Information Sciences, 271, 196-212.
Afful-Dadzie, E., Nabareseh, S., Oplatkova, Z.K., Klimek, P., 2016. Model for Assessing Quality of Online Health Information: A Fuzzy VIKOR Based Method, Journal of Multi-Criteria Decision Analysis, 23(1-2), 49-62.
Nilashi, M, Ahmadi, H., Ahani, A., Ravangard, R., Bin Inrahim, O., 2016. Determining the Importance of Hospital Information System Adoption Factors Using Fuzzy Analytic Network Process (ANP), Technological Forecasting & Social Change, 111, 244-264.
Chou, S.Y., Yu, V.F., Dewabharata, A., Dat, L.Q., 2012. A Fuzzy TOPSIS Approach for Medical Provider Selection and Evaluation, Proceedings of 2012 International Conference on Fuzzy Theory and Its Applications, 16-18, National Chung Hsing University, Taichung, Taiwan.
Lu, M.T., Lin, S.W., Tzeng, G.H., 2013. Improving RFID Adoption in Taiwan's Healthcare Industry Based on DEMATEL Technique with a Hybrid MCDM Model, Decision Support Systems, 56, 259-269.
Liou, J.J.H., Lu, M.T., Hu, S.K., Cheng, C.H., Chuang, Y.C., 2017. A Hybrid MCDM Model for Improving the Electronic Health Record to Better Serve Client Needs, Sustainability, 9(10), 1-13.
Kulak, O., Goren, H.G., Supciller, A.A., 2015. A New Multi Criteria Decision Making Approach for Medical Imaging Systems Considering Risk Factors, Applied Soft Computing, 35, 931-941.
Akdag, H., Kalaycı, T., Karagoz, S., Zulfikar, H., Giz, D., 2014. The Evaluation of Hospital Service Quality by Fuzzy MCDM, Applied Soft Computing, 23, 239-248.
Vachova, L., Hajdikova, T., 2017. Evaluation of Czech Hospitals Performance Using MCDM Methods, Proceedings of the World Congress on Engineering and Computer Science 2017, 1-4, San Francisco, USA.
Buyukozkan, G., Ciftci, G., 2012. A Combined Fuzzy AHP and Fuzzy TOPSIS Based Strategic Analysis of Electronic Service Quality in Healthcare Industry, Expert Systems with Applications, 39(3), 2341-2354.
Evaluating Health-Care Waste Treatment Technologies Using a Hybrid Multi-Criteria Decision Making Model, Renewable and Sustainable Energy Reviews, 41, 932-942.
Lu, C., You, J.X., Liu, H.C., Li, P., 2016. Health-Care Waste Treatment Technology Selection Using the Interval 2-Tuple Induced TOPSIS Method, International Journal of Environmental Research and Public Health, 13(6), 1-16.
Tanino, T., Tanaka, T., Inuiguchi, M., 2003. Multi-Objective Programming and Goal Programming Theory and Applications, First Edition, Springer-Verlag Berlin Heidelberg.
Saaty, T.L., 1994. How to Make a Decision: The Analytic Hierarchy Process, Interface, 19-43.
Vargas, L.G., 1990. An Overview of the Analytic Hierarchy Process and its Applications, European Journal of Operational Research, 48(1), 2-8.
Hwang, C.L., Yoon, K., 1981. Multiple Attributes Decision Making Methods and Applications, Springer, Berlin Heidelberg.
Zadeh, L.A., 1965. Fuzzy Sets, Information and Control, 8, 338-353.
Chen, C.T., 2000. Extensions of the TOPSIS for Group Decision Making Under Fuzzy Environment, Fuzzy Sets and Systems, 114, 1-9.
Chen, C.T., Lin, C.T., Huang, S.F., 2006. A Fuzzy Approach for Supplier Evaluation and Selection in Supply Chain Management, International Journal of Production Economics, 102(2), 289-301.