Hastane hizmet kalitesinin bulanık swara ve bulanık fucom yöntemlerine dayalı olarak değerlendirilmesine yönelik model önerisi

Yıl 2025, Cilt: 40 Sayı: 2, 1385 - 1400, 03.02.2025

Memiş Karaca , Özgür Demirtaş , Yılmaz Delice

https://doi.org/10.17341/gazimmfd.1533978

Öz

Hizmetlerin kalite değerlendirmesi her geçen gün zorlaşsa da hem işletmeler hem de bireyler için oldukça önem arz eden konuların başında yer almaktadır. Bu durum hastanelerin artan rekabet şartları içinde daha dikkatli ve ehemmiyetli adımlar atması gerekliliğini ortaya çıkarmıştır. Bu dikkatlilik ve ehemmiyetliliğin en temel ölçeği sağlık hizmetlerinde kalite olgusu olarak karşımıza çıkmaktadır. Sağlık alanında sürekli değişimler yaşanmasına rağmen sağlık hizmetlerinin kalitesinin ne olduğu, olacağı ve nasıl değerlendirileceği meselesinin her dönemin konusu olacağı düşünülmektedir. Bu çalışmada hastane hizmet kalitesinin Bulanık SWARA ve Bulanık FUCOM yöntemlerine dayalı olarak değerlendirilmesine yönelik model önerisi yapılması amaçlanmıştır. Bu kapsamda, literatür taraması ve alanında uzman sağlık personelleri ile belirlenen hastane hizmet kalitesine etki eden kriterlere ait önem ağırlıklarının Bulanık SWARA ve Bulanık FUCOM yöntemleri ile hesaplanması ve karşılaştırılması yapılmıştır. Bulanık SWARA yöntemine göre yapılan analizde ana kriterler açısından en önemli kriter ekipman ve tesisler olurken, bulanık FUCOM yöntemi ile yapılan analizde güvence kriterinin en önemli kriter olduğu tespit edilmiştir. Bulanık SWARA yöntemine göre alt kriterler nezdinde en önemli kriterler; temizlik ve hijyen, doğruluk, gizlilik, iletişim, deneyim, teşhis ve tedavi için gerekli donanımlar ve randevu veya muayene sırası alma kolaylığı olduğu tespit edilirken Bulanık FUCOM yöntemine göre alt kriterler nezdinde en önemli kriterlerin; temizlik ve hijyen, doğruluk, gizlilik, yardımseverlik, farklı tıbbi branşların varlığı, tıbbi ekipman ve randevu veya muayene sırası alma kolaylığı olduğu tespit edilmiştir.

Anahtar Kelimeler

Hizmet Kalitesi, Bulanık Swara, Bulanık Fucom

Etik Beyan

Kayseri Üniversitesi Etik Kurul’dan E.65764 sayı ile etik onay alınmıştır.

A model proposal for the evaluation of hospital service quality based on fuzzy swara and fuzzy fucom methods

Öz

Although the quality assessment of services is becoming increasingly difficult, it is one of the most important issues for both businesses and individuals. This situation has revealed the necessity for hospitals to take more careful and important steps in increasing competition conditions. The most basic scale of this caution and importance is the phenomenon of quality in health services. Although there are continuous changes in the field of health, it is thought that the issue of what the quality of health services is, will be and how to evaluate it will be the subject of every period. In this study, it is aimed to propose a model for the evaluation of hospital service quality based on Fuzzy SWARA and Fuzzy FUCOM methods. In this context, the importance weights of the criteria affecting hospital service quality, which were determined by literature review and expert healthcare personnel, were calculated and compared with Fuzzy SWARA and Fuzzy FUCOM methods. According to the Fuzzy SWARA method, the most important criterion in terms of main criteria is equipment and facilities, while the Fuzzy FUCOM method analysis shows that the assurance criterion is the most important criterion. According to the Fuzzy SWARA method, the most important criteria in terms of sub-criteria are cleanliness and hygiene, accuracy, confidentiality, communication, experience, equipment for diagnosis and treatment and ease of making an appointment or examination queue, while according to the Fuzzy FUCOM method, the most important criteria in terms of sub-criteria are cleanliness and hygiene, accuracy, confidentiality, helpfulness, availability of different medical branches, medical equipment and ease of making an appointment or examination queue.

Kaynakça

• 1. Bircher, J., Towards a Dynamic Definition of Health and Disease. Med Health Care Philos, 8 (3), 335–41, 2005.
• 2. Organisation for Economic Co-operation and Development. OECD Health Data 2022. https://stats.oecd.org/Index.aspx?DataSetCode=SHA.Yayın tarihi 2023. Erişim tarihi 25.10.2023
• 3. T.C. Sosyal Güvenlik Kurumu (SGK), (2022), Sosyal Güvenlik Temel Göstergeleri- Nisan, erişim adresi: https://www.sgk.gov.tr/Istatistik/Aylik/42919466-593f-4600-937d-1f95c9e252e6/ Yayın tarihi 2023. Erişim Tarihi: 25.10.2023.
• 4. Agarwal, S., Kant, R., & Shankar, R., Evaluating solutions to overcome humanitarian supply chain management barriers: A hybrid fuzzy SWARA–Fuzzy WASPAS approach, International Journal of Disaster Risk Reduction, 51, 101838, 2020.
• 5. Eghbali-Zarch, M., Tavakkoli-Moghaddam, R., Esfahanian, F., Sepehri, M. M., & Azaron, A., Pharmacological therapy selection of type 2 diabetes based on the SWARA and modified MULTIMOORA methods under a fuzzy environment, Artificial intelligence in medicine, 87, 20-33, 2018.
• 6. Mishra, A. R., Rani, P., Pandey, K., Mardani, A., Streimikis, J., Streimikiene, D., & Alrasheedi, M., Novel multi-criteria intuitionistic fuzzy SWARA–COPRAS approach for sustainability evaluation of the bioenergy production process, Sustainability, 12 (10), 4155, 2020.
• 7. Mardani, A., Saraji, M.K., Mishra, A.R. ve Rani, P., A novel extended approach under hesitant fuzzy sets to design a framework for assessing the key challenges of digital health interventions adoption during the COVID-19 outbreak, Applied Soft Computing, 96, 106613, 2020.
• 8. Ünlü A., Gezmişoğlu G., Çağıl G., Supplier evaluation with factor analysis based hybrid SWARA-VIKOR methods, Journal of the Faculty of Engineering and Architecture of Gazi University, 38 (4), 2231-2240, 2023.
• 9. Dündar S., Selection of compost plant location by K-Means and ARAS methods in TR83 region, Journal of the Faculty of Engineering and Architecture of Gazi University, 38 (4), 2607-2624, 2023.
• 10. Mitrović Simić, J., Stević, Ž., Zavadskas, EK, Bogdanović, V., Subotić, M. and Mardani, A., A Novel CRITIC-Fuzzy FUCOM-DEA-Fuzzy MARCOS Model for Safety Evaluation of Road Sections Based on Geometric Parameters of Road. Symmetry, 12 (12), 2006, 2020.
• 11. Xu, D., Ren, J., Dong, L., & Yang, Y., Portfolio selection of renewable energy-powered desalination systems with sustainability perspective: a novel MADM-based framework under data uncertainties, Journal of Cleaner Production, 275, 124114, 2020.
• 12. Demir, G., E-Devlet Web Sitelerinin Bulanık FUCOM ile Değerlendirilmesi, Uluslararası Ekonomik ve İdari Akademik Araştırmalar Dergisi, 1 (2), 41-53, 2021.
• 13. Demir, G., Damjanović, M., Matović, B. and Vujadinović, R., Toward Sustainable Urban Mobility by Using Fuzzy-FUCOM and Fuzzy-CoCoSo Methods: The Case of the SUMP Podgorica, Sustainability, 14 (9), 4972, 2022.
• 14. Peker, BN ve Görener, A., Tesis Yeri Seçiminde Kriterlerin Önem Ağırlıklarının Bulanık FUCOM Yöntemiyle Belirlenmesi, İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi, 21 (45), 1512-1536, 2022.
• 15. Uslu G., Erdebilli B., Examination of fire station location selection using the FUZZY FUCOM method, Journal of the Faculty of Engineering and Architecture of Gazi University, 39 (4), 2373-2382, 2024.
• 16. Köseoğlu, A., Intuitionistic multiplicative set approach for green supplier selection problem using TODIM method. Journal of Universal Mathematics, 5 (2), 149-158, 2022.
• 17. Köseoğlu, A., A., comparative decision-making for electronic product purchases during a pandemic, Gümüşhane Üniversitesi Fen Bilimleri Dergisi, 109-118, 2022.
• 18. Köseoğlu, A., Şahin, R., Merdan, M., A., simplified neutrosophic multiplicative set‐based TODIM using water‐filling algorithm for the determination of weights, Expert Systems, 37 (4), e12515, 2020.
• 19. Akman G., Kokumer Z., Evaluation of digital transformation competency in the white-goods sector in the context of Industry 4.0 by MACBETH and EDAS methods, Journal of the Faculty of Engineering and Architecture of Gazi University, 38 (4), 2033-2054, 2023.
• 20. Nebati E.E., Ekmekçi İ., Başlıgil H., Proposal of index model in performance measurement: Shopping mall application, Journal of the Faculty of Engineering and Architecture of Gazi University, 38 (3), 1403-1416, 2023.
• 21. Piriştine, B., Bulanık SWARA yöntemi ile işletmelerin kurumsallık düzeyinin belirlenmesi, Yüksek Lisans Tezi, Sakarya Universitesi, Fen Bilimleri Enstitüsü, Sakarya, 2019.
• 22. Keršuliene, V., Zavadskas, E.K., Turskİs, Z., Selection of Rational Dispute Resolution Method by Applying New Step‐Wise Weight Assessment Ratio Analysis (SWARA), Journal Of Business Economics And Management, 11 (2), 243-258, 2010.
• 23. Çakır, E., Akar, G. S., Bütünleşik SWARA-TOPSIS yöntemi ile makine seçimi: bir üretim işletmesinde uygulama, International Journal of Academic Value Studies, 3 (13), 206-216, 2017.
• 24. Yurdoğlu H., Kundakçı N., SWARA ve WASPAS Yöntemleri ile Sunucu Seçimi, Balıkesir Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 20 (38), 253-269, 2017.
• 25. Ayçin, E., Çok kriterli karar verme: Bilgisayar uygulamalı çözümler, Nobel Akademik Yayıncılık, Ankara, 2019.
• 26. A Ghorshı Nezhad, M.R., Zolfanı, S.H., Moztarzadeh, F., Zavadskas, E.K., Bahramım.,. 'Planning the Priority of High Tech Industries based on SWARA-WASPAS Methodology: The Case of the Nano Technology in İran, Ekonomska istrazivanja, 28 (1), 1111-1137, 2015.
• 27. Sumrit, D., Supplier Selection for Vendor-Managed Inventory in Healthcare Using Fuzzy Multi-Criteria Decision Making, Decision Science Letters, 9 (2), 233-256, 2020.
• 28. Pamučar, D., Stević, Ž., Sremac, S., A new model for determining weight coefficients of criteria in MCDM models: full consistency method (FUCOM), Symmetry, 10 (9), 2018.
• 29. Pamučar, D., Deveci, M., Canıtez, F., Božanić, D., A fuzzy Full Consistency Method-Dombi-Bonferroni model for prioritizing transportation demand management measures, Applied Soft Computing, 87, 105952, 2020.
• 30. Ecer, F., Çok Kriterli Karar Verme: Geçmişten Günümüze Kapsamlı Bir Yaklaşım, Seçkin Yayınevi, Ankara, 2020.
• 31. Taşkent, M.C., Tedarikçi Seçimi İçin Kriter Ağırlıklarının Eniyilenmesi Temelli Yeni Bir Bulanık Çok Kriterli Karar Verme Yaklaşımı, Yüksek Lisans Tezi, Atatürk Üniversitesi, Fen Bilimleri Enstitüsü, Erzurum, 2021.
• 32. Sürücü, L., Liderliğin Karanlık Yüzü, Orion Akademi, Ankara, 2022.
• 33. Demirdöğen, O. Erdal, H., Akbaba, A.İ., The analysis of factors that affect innovation performance of logistics enterprises in Turkey, In German-Turkish Perspectives on IT and Innovation Management: Challenges and Approaches, Springer Fachmedien Wiesbaden, 143-164, 2018.
• 34. Parasuraman A, Zeıthaml V, Berry Ll., A Conceptual Model of Service Quality and It’s Implications for Future, Journal of Marketing, 49, 41-50, 1985.
• 35. Parasuraman A, Zeıthaml V, Berry Ll., Servqual: A Multiple-Item Scale for Measuring Consumer Perceptions of Service Quality, Journal of Retailing, 64 (1), 12-40, 1988.
• 36. Cronin Jr, J. J., Taylor, S. A., Measuring service quality: a reexamination and extension. Journal of marketing, 56 (3), 55-68, 1992.
• 37. Haddad, S., Fournier, P., Machouf, N, Yatara, F., What does quality mean to lay people? Community perceptions of primary health care services in Guinea, Social Science & Medicine, 47 (3), 381-94, 1998.
• 38. Lee, H., Delene, L. M., Bunda, M. A., Kim, C., Methods of measuring health-care service quality, Journal of Business Research, 48 (3), 233–246, 2000.
• 39. Gross R., A consumer-based tool for evaluating the quality of health services in the Israeli health care system following reform, Health Policy, 68 (2), 143-58, 2004.
• 40. Miller, D.C., Montie, J.E., Wei, J.T., Measuring the quality for localized prostate cancer, The Journal of Urology, 174 (2), 425–431, 2005.
• 41. Pakdil, F., Timothy, N. H., Patient Satisfaction in a Preoperative Assessment Clinic: An Analysis Using SERVQUAL Dimensions, Total Quality Management, 16 (1), 2005.
• 42. A Lee, M.A.,Yom, Y.H.A., Comparative Study of Patients’ and Nurses’ Perceptions of Quality of Nursing Services, Satisfaction and Intent to Revisit the Hospital, International Journal of Nursing Studies 44 (4), 545-55, 2006.
• 43. Kruk, M. E., Freedman, L. P., Assessing health system performance in developing countries: A review of the literatüre, Health Policy, 85 (3), 263–276, 2008.
• 44. Arasli, H., Ekiz, E. H., Katircioglu S.T., Gearing service quality into public and private hospitals in small islands: empirical evidence from Cyprus, International Journal of Health Care Quality Assurance, 21 (1), 8-23, 2008.
• 45. Taş D., Sağlık Hizmet Kalitesinin Ölçümüne İlişkin Bir Uygulama, Yüksek Lisans Tezi, İstanbul Üniversitesi, Sosyal Bilimler Enstitüsü, İstanbul, 2009.
• 46. Hsu, T.-H., Pan, F.F.C., Application of Monte Carlo AHP in ranking dental quality attributes, Expert Systems with Applications, 36 (2), 2310-2316, 2009.
• 47. Büyüközkan, G., Çiftçi, G., Güleryüz, S., Strategic analysis of healthcare service quality using fuzzy AHP methodology, Expert Systems with Applications, 38, 9407–9424, 2011.
• 48. Büyüközkan, G., Çifçi, G., 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, 2012.
• 49. Chang, T. H., Fuzzy VIKOR method: A case study of the hospital service evaluation in Taiwan. Information Sciences, 271, 196-212, 2014.
• 50. Akdağ, H., Kalaycı, T., Karagöz, S., Zülfikar, H., Giz, D., The evaluation of hospital service quality by fuzzy MCDM, Applied Soft Computing, 23, 239-248, 2014.
• 51. Chui, K. T., Tsang, K. F., Wu, C. K., Hung, F. H., Chi, H. R., Chung, H. S.-H., Man, K. F. and Ko, K. T., Cardiovascular diseases identification using electrocardiogram health identifier based on multiple criteria decision making, Expert Systems with Applications, 42 (13), 5684-5695, 2015.
• 52. Lupo, T., A fuzzy framework to evaluate service quality in the healthcare industry: An empirical case of public hospital service evaluation in Sicily, Applied Soft Computing, 40, 468-478, 2016.
• 53. Cooperberg, M.R., Birkmeyer J.D., Litwin, M.S., Defining high quality healthcare, Urologic Oncology: Seminars and Original Investigations, 27 (4), 411-416, 2009.
• 54. Ulkhaq, M. M., Fidiyanti, F., Raharjo, M. F. M., Siamiaty, A. D., Sulistiyani, R. E., Akshinta, P. Y., Nugroho, E. A., Evaluating hospital service quality: A combination of the AHP and TOPSIS, In Proceedings of the 2nd International Conference on Medical and Health Informatics, 117-124, 2018.
• 55. Torkzad, A., ve Beheshtinia, M. A., Evaluating and prioritizing hospital service quality, International Journal of health care quality assurance, 32 (2), 332-346, 2019.
• 56. Derici, S., Doğan, N.Ö., Hasta Perspektifinden Hastanelerde Hizmet Kalitesi: Bulanık AHP ile Bir Uygulama, Nevşehir Hacı Bektaş Veli Üniversitesi SBE Dergisi, 11 (3), 1155-1177, 2021.
• 57. KhanMohammadi, E., Talaie, H., Azizi, M., A healthcare service quality assessment model using a fuzzy best–worst method with application to hospitals with in-patient services, Health Analysis, 4, 100241, 2023.
• 58. Şenol M.B., Yılmaz N., A model and application of occupational health and safety risk assessment, Journal of the Faculty of Engineering and Architecture of Gazi University, 32 (1), 77-87, 2017.
• 59. Beylihan, E., Elevli, S., Hata Türü ve Etkileri Analizinde Bulanık SWARA Yönteminin Kullanımı: Otomotiv Sektörü Örneği, Mühendislik Bilimleri ve Tasarım Dergisi, 11 (1), 212-224, 2023.
• 60. Taşkent, MC, Delice, E.K., Bulanık FUCOM yöntemi ile tedarikçi değerlendirme kriterlerinin ağırlıklarının belirlenmesi, Avrupa Bilim ve Teknoloji Dergisi, (28), 863-868, 2021.
• 61. Şengül, D., Çağıl, G., Bulanık SWARA ve bulanık analitik hiyerarşi prosesi yöntemi ile iş değerlemesi, Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 11 (3), 965-976, 2020.
• 62. Kılıç, R., Emeç, Ş., Erkayman, B., Integrated fuzzy FUCOM and fuzzy MARCOS approaches for housing location problem, Brilliant Engineering, 3 (4), 4727, 2022.
• 63. Kara, A., Lonial, S., Tarim, M., Zaim, S., A paradox of service quality in Turkey, European Business Review, 17 (1), 5–20, 2005.
• 64. Choi, K.-S., Lee, H., Kim, C., Lee, S., The service quality dimensions and patient satisfaction relationships in South Korea: Comparisons across gender, age and types of service, Journal of Services Marketing, 19 (3), 140–149, 2005.
• 65. Kıdak, L. B., Arslan, E. T., Burmaoğlu, S., Hastanın Sesi Duyuluyor Mu? Bir Devlet Hastanesinde Bulanık AHP ile Ağırlıklandırılmış Kalite Fonksiyon Göçerimi Uygulaması, Journal of Alanya Faculty of Business/Alanya Isletme Fakültesi Dergisi, 8 (2), 2016.
• 66. Pekkaya, M., İmamoğlu, Ö. P., Hastane Hizmet Kalitesinde Servqual Boyutlarının Önem Derecelerinin Belirlenmesi, Uluslararası Yönetim İktisat ve İşletme Dergisi, ICMEB17 Özel Sayısı, 607-616, 2017.
• 67. Singh, A., Prasher, A., Measuring healthcare service quality from patients perspective: using Fuzzy AHP application, Total Quality Management & Business Excellence, 30 (3-4), 284-300, 2019.
• 68. Dinçer, H., Yüksel, S., Gökalp, Y., Eti, S., SERVQUAL-based evaluation of service quality in Turkish health industry with fuzzy logic, In Interdisciplinary perspectives on operations management and service evaluation, IGI Global, 213-233, 2020.