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Türkiye Sağlık Sistemi Kapasitesinin Ağırlıklandırma Yöntemlerine Dayalı WISP Yöntemi ile İl Düzeyinde Değerlendirilmesi

Yıl 2025, Cilt: 59 Sayı: 1, 143 - 166, 22.01.2025
https://doi.org/10.51551/verimlilik.1549245

Öz

Amaç: Bu çalışmanın birincil amacı, Türkiye sağlık sisteminin kapasite ve kapasite açıklarını il düzeyinde göreceli olarak değerlendirmektir. Çalışmanın ikincil amacı ise R programlama dilinde kullanılan ağırlıklandırma yöntemleri için uygulama algoritmaları geliştirmektir.
Yöntem: Sağlık sistemi kapasitesinin değerlendirilmesinde kullanılan karar kriterleri CRITIC, Shannon Entropy ve NMV yöntemleri ile ağırlıklandırılmıştır. İllerin sağlık sistemi kapasitesini değerlendirmek için WISP yöntemi kullanılmıştır. Veriler Sağlık Bakanlığı'nın 2022 Sağlık İstatistikleri Yıllığı'ndan alınmıştır.
Bulgular: Tunceli, Bayburt ve Kilis, CRITIC tabanlı WISP skorlarına göre Türkiye'de sağlık sistemi kapasitesi açısından 81 il arasında optimal çözüme en yakın üç ildir. Buna karşılık, Bursa, İstanbul ve Şanlıurfa optimal çözümden en uzak üç ildir.
Özgünlük: İl düzeyinde, sağlık sisteminin kapasitesindeki boşlukları tespit edebilir ve geliştirebiliriz. Kendi kendine yeterli sağlık sistemi kapasitesi oluşturabilir ve sağlık sistemini daha dirençli hale getirilebilir. Öte yandan, ağırlıklandırma yöntemleri için uygulama algoritmalarının geliştirilmesi önemli bir katkıdır. Böylece karar vericiler küçük ve özellikle büyük ölçekli veri setleri üzerinde anlık çözümler üretebilir.

Kaynakça

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Evaluation of Turkish Health System Capacity at Provincial Level by WISP Method Based on Weighting Methods

Yıl 2025, Cilt: 59 Sayı: 1, 143 - 166, 22.01.2025
https://doi.org/10.51551/verimlilik.1549245

Öz

Purpose: The study aims to evaluate the capacity and capacity gaps of the Turkish health system at the provincial level in relative terms. The secondary objective of the study is to develop application algorithms for the weighting methods utilized in the R programming language.
Methodology: The decision criteria used in evaluation of health system capacity were weighted by CRITIC, Shannon Entropy, and NMV methods. The WISP method was used to evaluate the health system capacity of provinces. Data were drawn from the Ministry of Health's Health Statistics Yearbook for 2022.
Findings: Tunceli, Bayburt, and Kilis are the three provinces closest to the optimal solution among 81 provinces in terms of health system capacity in Türkiye, according to CRITIC-based WISP scores. On the contrary, Bursa, İstanbul and Şanlıurfa are the three provinces furthest from an optimal solution.
Originality: At the provincial level, gaps in the health system's capacity can be identified and subsequently improved. It is possible to develop self-sufficient health system capacity and enhance its resilience. The development of application algorithms for weighting methods makes a significant contribution. Decision makers are capable of generating immediate solutions for both small and large-scale data sets using the algorithms.

Etik Beyan

It was declared by the author that scientific and ethical principles have been followed in this study and all the sources used have been properly cited.

Kaynakça

  • Adalı, E.A. and Tuş, A. (2019). “Hospital Site Selection with Distance-Based Multi-Criteria Decision-Making Methods”, International Journal of Healthcare Management, 14(2), 534-544. https://doi.org/10.1080/20479700.2019.1674005
  • Allaire, J., Xie, Y., Dervieux, C., McPherson, J., Luraschi, J., Ushey, K, Atkins, A., Wickham, H., Cheng, J., Chang, W. and Iannone, R. (2024). “rmarkdown: Dynamic Documents for R. R Package Version 2.26.”.
  • Arnold, J. (2024). “ggthemes: Extra Themes, Scales and Geoms for 'ggplot2'”, R Package Version 5.1.0.
  • Bağcı H. and Sarıay, İ. (2021). “The Role of Market Value and Market Value Initial Public Offering In Business Performance: An Applıcatıon in the Istanbul Stock Exchangepublic Offering Index”, Finansal Araştırmalar ve Çalışmalar Dergisi,13(24), 36-54. https://doi.org/10.14784/marufacd.880613.
  • Baydaş, M. and Pamučar, D. (2022). “Determining Objective Characteristics of MCDM Methods under Uncertainty: An Exploration Study with Financial Data”, Mathematics, 10(7), 1115. https://doi.org/10.3390/math10071115
  • Bhaskar, A.S. and Khan, A. (2022). “Comparative Analysis of Hybrid MCDM Methods in Material Selection for Dental Applications”, Expert Systems with Applications, 209, 1-8. https://doi.org/10.1016/j.eswa.2022.118268
  • Bivand, R., Pebesma, E. and Gomez-Rubio, V. (2013). “Applied Spatial Data Analysis with R”, Second Edition. Springer, NY.
  • Blanchet, K., Nam, S.L., Ramalingam, B. and Pozo-Martin, F. (2017). “Governance and Capacity to Manage Resilience of Health Systems: Towards a New Conceptual Framework”, International Journal of Health Policy and Management, 1, 6(8), 431-435. https://doi.org/10.15171/ijhpm.2017.36
  • Broekhuizen, H., Groothuis-Oudshoorn, C.G., van Til, J.A., Hummel, J.M. and IJzerman, M.J. (2015). “A Review and Classification of Approaches for Dealing with Uncertainty in Multi-Criteria Decision Analysis for Healthcare Decisions”, Pharmacoeconomics, 33(5), 445-455. https://doi.org/10.1007/s40273-014-0251-x
  • Bulut, T. (2017). “Çok Kriterli Karar Verme (ÇKKV) Modellerinde Kriterlerin Ağırlıklandırılmasına Yönelik Bir Model Önerisi: Normalize Edilmiş Maksimum Değerler [NMD] Metodu (Normalized Maximum Values [NMV] Method, https://tevfikbulutcom.wordpress.com/2017/06/21/coklu-karar-verme-modellerinde-kriterlerin-agirliklandirilmasina-yonelik-model-onerisi, (Accessed: 15.05.2024).
  • Bulut, T. (2022a). “R’da Normalize Edilmiş Maksimum Değerler [NMD] Uygulama Algoritması”, https://tevfikbulut.net/rda-normalize-edilmis-maksimum-degerler-nmd-metodu, (Accessed: 15.05.2024).
  • Bulut, T. (2022b). “Normalize Edilmiş Maksimum Değerler [NMD] Metodunun Teorik Çerçevesi”, https://tevfikbulut.net/normalize-edilmis-maksimum-degerler-nmd-metodu, (Accessed: 15.05.2024).
  • Chen, J., Guo, X., Pan, H. and Zhong, S. (2021). “What Determines City's Resilience Against Epidemic Outbreak: Evidence from China's COVID-19 Experience”, Sustainable Cities and Society, 70, 102892. https://doi.org/10.1016/j.scs.2021.102892
  • Ciardiello, F. and Genovese, A. (2023). “A Comparison between TOPSIS and SAW Methods”, Annals of Operations Research, 325:967-994. https://doi.org/10.1007/s10479-023-05339-w
  • Delgado, A., Huamani, A. and Brillitt, B. (2018). “Applying Shannon Entropy to Analise Health System Level by departments in Peru”, 2018 IEEE XXV International Conference on Electronics, Electrical Engineering and Computing (INTERCON), Lima, Peru, 1-4. https://doi.org/10.1109/INTERCON.2018.8526435
  • Diakoulaki, D., Mavrotas, G. and Papayannakis, L. (1995). “Determining Objective Weights in Multiple Criteria Problems: The CRITIC Method”, Computers & Operations Research, 22(7), 763-770.
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  • Feng, J., Guo, Z., Ai, L., Liu, J., Zhang, X., Cao, C., Xu, J., Xia, S., Zhou, X., Chen, J. and Li, S. (2022). “Establishment of an Indicator Framework for Global One Health Intrinsic Drivers Index Based on the Grounded Theory and Fuzzy Analytical Hierarchy-Entropy Weight Method”, Infectious Diseases of Poverty, 11, 121. https://doi.org/10.1186/s40249-022-01042-3
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  • Kumar, R., Singh, S., Bilga, P. S., Jatin, N., Singh, J., Singh, S., Scutaru, M. and Pruncu, C. I. (2021). “Revealing the Benefits of Entropy Weights Method for Multi-Objective Optimization in Machining Operations: A Critical Review”, Journal of Materials Research and Technology, 10:1471-1492. https://doi.org/10.1016/j.jmrt.2020.12.114
  • Lee, H.C. and Chang, C.T. (2018). “Comparative Analysis of MCDM Methods for Ranking Renewable Energy Sources in Taiwan”, Renewable and Sustainable Energy Reviews, 92:883-896. https://doi.org/10.1016/j.rser.2018.05.007
  • Lin, Y., Alshehri, Y., Alnazzawi, N., Abid, M., Khan, S.A., Jabeen, F. and Elwarfalli, I. (2023) RETRACTED ARTICLE: Social Media Analytics and Their Applications to Evaluate an Activity in Online Health İnterventions Using CRITIC and TOPSIS Techniques”, Soft Computing. https://doi.org/10.1007/s00500-023-08004-3
  • Microsoft Corporation. (2018). “Microsoft Excel”, https://office.microsoft.com/excel, (Accessed: 03.04.2024].
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  • Németh, B., Molnár, A., Bozóki, S., Wijaya, K., Inotai, A., Campbell, J. D. and Kaló, Z. (2019). “Comparison of Weighting Methods Used in Multicriteria Decision Analysis Frameworks in Healthcare with Focus on Low- and Middle-Income Countries”, Journal of Comparative Effectiveness Research, 8(4), 195-204. https://doi.org/10.2217/cer-2018-0102
  • Paradowski, B., Shekhovtsov, A., Baczkiewicz, A., Kizielewicz, B. and Sałabun, W. (2021). “Similarity Analysis of Methods for Objective Determination of Weights in Multi-Criteria Decision Support Systems”, Symmetry, 13:1874. https://doi.org/10.3390/sym13101874
  • Pebesma, E. and Bivand, R. (2005). “Classes and Methods for Spatial Data in R”, R News, 5(2), 9-13.
  • Pebesma, E. and Bivand, R. (2023). “Spatial Data Science: With applications in R”, Chapman and Hall/CRC. https://doi.org/10.1201/9780429459016
  • Peng, X., Krishankumar, R. and Ravichandran, K.S. (2021). “A Novel Interval-Valued Fuzzy Soft Decision-Making Method Based on CoCoSo and CRITIC for Intelligent Healthcare Management Evaluation”, Soft Computing, 25, 4213-4241. https://doi.org/10.1007/s00500-020-05437-y
  • Pourmohammadi, K., Shojaei, P., Rahimi, H. and Bastani, P. (2018). “Evaluating the Health System Financing of the Eastern Mediterranean Region (EMR) Countries Using Grey Relation Analysis and Shannon Entropy”, Cost Effectiveness and Resource Allocation, 16, 31. https://doi.org/10.1186/s12962-018-0151-6
  • Pramanik, P.K.D., Biswas, S., Pal, S., Marinković, D. and Choudhury, P. (2021). “A Comparative Analysis of Multi-Criteria Decision-Making Methods for Resource Selection in Mobile Crowd Computing”, Symmetry, 13, 1713. https://doi.org/10.3390/sym13091713
  • R Core Team. (2024). “R: A Language and Environment for Statistical Computing”, R Foundation for Statistical Computing, Vienna, Austria.
  • Rizzo, M. and Szekely, G. (2022). “energy: E-Statistics: Multivariate Inference via the Energy of Data”, R package version 1.7-11.
  • Salehi, V., Moradi, G., Omidi, L. and Rahimi, E. (2023). “An MCDM Approach to Assessing Influential Factors on Healthcare Providers’ Safe Performance during the COVID-19 Pandemic: Probing into Demographic Variables”, Journal of Safety Science and Resilience, 4(3), 274-83. https://doi.org/10.1016/j.jnlssr.2023.05.002
  • Schauberger, P. and Walker, A. (2023). “openxlsx: Read, Write and Edit xlsx Files”, R Package Version 4.2.5.2.
  • Shamasunder, S., Holmes, S. M., Goronga, T., Carrasco, H., Katz, E., Frankfurter, R. and Keshavjee, S. (2020). “COVID-19 Reveals Weak Health Systems by Design: Why We Must Re-Make Global Health in This Historic Moment”, Global Public Health, 15(7),1083-1089. https://doi.org/10.1080/17441692.2020.1760915
  • Shannon, C.E. (1948). “A Mathematical Theory of Communication”, The Bell System Technical Journal, 27(3), 379-423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
  • Shekhovtsov, A. (2021). “How Strongly Do Rank Similarity Coefficients Differ Used in Decision Making Problems?”, Procedia Computer Science, 192, 4570-4577. https://doi.org/10.1016/j.procs.2021.09.235
  • Shemshadi, A., Shirazi, H., Toreihi, M. and Tarokh, M.J. (2011). “A Fuzzy VIKOR Method for Supplier Selection Based on Entropy Measure for Objective Weighting”, Expert Systems with Applications, 38, 12160-12167. https://doi.org/10.1016/j.eswa.2011.03.027
  • Song, M., Zhu, Q., Peng, J. and Santibanez Gonzalez, E.D.R. (2017). “Improving the Evaluation of Cross Efficiencies: A Method Based on Shannon Entropy Weight”, Computers & Industrial Engineering, 112, 99-106. https://doi.org/10.1016/j.cie.2017.07.023
  • Stanujkic, D., Popovic, G., Karabasevic, D., Meidute-Kavaliauskiene, I. and Ulutaş, A. (2023). “An Integrated Simple Weighted Sum Product Method-WISP”, IEEE Transactions on Engineering Management, 70(5), 1933-1944. https://doi.org/10.1109/TEM.2021.3075783
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  • World Health Organization (WHO). (2024b). “Health System Resilience Indicators: An Integrated Package for Measuring and Monitoring Health System Resilience in Countries”, https://iris.who.int/bitstream/handle/10665/376071/9789240088986-eng.pdf?sequence=1, (Accessed: 15.05.2024).
  • Zamani-Sabzi, H., King, J.P., Gard, C.C. and Abudu, S. (2016). “Statistical and Analytical Comparison of Multi-Criteria Decision-Making Techniques under Fuzzy Environment”, Operations Research Perspectives, 3(C), 92-117. https://doi.org/10.1016/j.orp.2016.11.001
  • Zavadskas, E.K., Stanujkić, D., Karabašević, D. and Turskis, Z. (2022). “Analysis of the Simple WISP Method Results Using Different Normalization Procedures”, Studies in Informatics and Control, 31(1), 5-12. https://doi.org/10.24846/v31i1y202201
  • Zhang, X. Y., Xu, H. Q., Wang, C. F., Shao, J., Wan, Y. H. and Tao, F. B. (2023). “Application of Entropy Weight TOPSIS Comprehensive Method in the Evaluation of Students' Physical Health Level”, Chinese Journal of Preventive Medicine, 57(7), 997-1003. https://doi.org/10.3760/cma.j.cn112150-20220712-00712
  • Zhao, L., Jin, Y., Zhou, L., Yang, P., Qian, Y., Huang, X. and Min, M. (2023). “Evaluation of Health System Resilience in 60 Countries Based on Their Responses to COVID-19”, Front Public Health, 10, 1081068. https://doi.org/10.3389/fpubh.2022.1081068
Toplam 70 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yöneylem
Bölüm Araştırma Makalesi
Yazarlar

Tevfik Bulut 0000-0002-3668-7436

Yayımlanma Tarihi 22 Ocak 2025
Gönderilme Tarihi 12 Eylül 2024
Kabul Tarihi 19 Kasım 2024
Yayımlandığı Sayı Yıl 2025 Cilt: 59 Sayı: 1

Kaynak Göster

APA Bulut, T. (2025). Evaluation of Turkish Health System Capacity at Provincial Level by WISP Method Based on Weighting Methods. Verimlilik Dergisi, 59(1), 143-166. https://doi.org/10.51551/verimlilik.1549245

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