Turkiye Klinikleri Journal of Health Sciences

.: ORIGINAL RESEARCH
Sağlıkta Verimlilik Karne Uygulamaları, Hastanelerin Etkinliğinin Retrospektif Olarak Stokastik Sınır Yaklaşımı ile Değerlendirilmesi: AI Rol Grubu Hastaneler Örneği
Health Efficiency Scorecard Applications, Retrospective Evaluation of Hospitals Efficiency with Stochastic Boundary Approach: The Case of AI Role Group Hospitals
Mehmet KOCAa, Yeter DEMİR USLUa
aİstanbul Medipol Üniversitesi Sağlık Bilimleri Fakültesi, Sağlık Yönetimi ABD, İstanbul, TÜRKİYE
Turkiye Klinikleri J Health Sci. 2022;7(1):212-20
doi: 10.5336/healthsci.2021-81180
Article Language: TR
Full Text
ÖZET
 Amaç: Sağlık Bakanlığı (SB) AI rol grubu hastanelerin etkinliğinin belirlenmesi ve hastanelerin etkinliğinin verimlilik karne uygulamaları başlangıcından itibaren nasıl değiştiğini belirlemek amacı ile yapılmıştır. Gereç ve Yöntemler: Stokastik Sınır Analizi (SSA), karar verme birimlerinin etkinliklerini ölçek için kullanılan parametrik bir yöntem olup; etkinsizliğin rassal hatadan mı yoksa etkinsizlikten mi kaynaklandığını ayırt edebilmesi nedeniyle son yıllarda yapılan çalışmalarda daha fazla kullanılmaya başlanmıştır. Bu nedenle, SB'ye bağlı 48 AI rol grubunda bulunan genel hastanelerin 2012 ve 2018 yılları arasındaki (7 yıllık panel veri) verileri belirlenen girdi ve çıktıları SSA yöntemi kullanılarak hesaplanmıştır. Bulgular: Çalışmada, öncelikle SSA modeli klasik regresyon modeline karşı test edilmiş ve daha sonra SSA yönteminde uygun modelin belirlenmesi için Battese ve Coelli (1992) modeline getirilen ƞ=0 kısıtının ve µ=0 kısıtının geçerliliği test edilmiştir. Test sonucunda ƞ parametresi 0,0086 (p=0,307) ve µ parametresi 0,2885 (p=0,156) olarak bulunmuştur. Her 2 parametrede %5 seviyesinde pozitif ve anlamsız bulunmuştur. Yapılan testler sonucunda klasik SSA modelleri arasında, Translog Üretim Fonksiyonu formunda oluşturulan Battese ve Coelli (1995) modelinin, eldeki veri setine en uygun model olduğu tespit edilmiştir. Analiz sonucunda hastanelerin etkinliği Model 1'de 0,25-0,93 arasında; Model 2'de 0,76-0,97 arasında olduğu tespit edilmiştir. Sonuç: Üretim sınırından sapmalar incelendiğinde, Model 1'de yaklaşık %29, Model 2'de %17'lik kısmın teknik etkinsizlikten (𝑢𝑖) kaynaklandığından bu alanda düzenleme yapılarak hastanelerin etkinliğinin artırılabileceği sonucuna ulaşılmıştır.

Anahtar Kelimeler: AI rol grubu hastaneler; hastanelerin etkinliği; stokastik sınır analizi
ABSTRACT
 Objective: The purpose of determining the efficiency of the AI role group hospitals of the Ministry of Health and determining how the efficiency of the hospitals have changed since the beginning of the efficiency report card applications was made. Material and Methods: Stochastic Frontier Analysis (SFA) is a parametric method used to scale the effectiveness of decision-making units; Due to the fact that it can distinguish whether ineffectiveness is caused by random error or inefficiency, it has been used more in recent years. For this reason, the inputs and outputs of the general hospitals in 48 AI role groups affiliated to the Ministry of Health between 2012 and 2018 (7-year panel data) were calculated using the SFA method. Results: In the study, the SFA model was first tested against the classical regression model and then the validity of the ƞ=0 constraint and the µ=0 constraint introduced to the Battese and Coelli (1992) model in order to determine the appropriate model in the SFA method. As a result of the test, the ƞ parameter was found to be 0.0086 (p=0.307) and the µ parameter as 0.2885 (p=0.156). Both parameters were found to be positive and insignificant at the 5% level. As a result of the tests, it was determined that Battese and Coelli (1995) model created in the form of Translog Generation Function among classical SFA models is the most suitable model for the data set. As a result of the analysis, the efficiency of hospitals is between 0.25 and 0.93 in Model 1; it has been found to be between 0.76 and 0.97 in Model 2. Conclusion: When the deviations from the production limit are examined, it is concluded that the efficiency of hospitals can be increased by making arrangements in this area, since approximately 29% in Model 1 and 17% in Model 2 are caused by technical inefficiency (𝑢𝑖).

Keywords: AI role group hospitals; efficiency of hospitals; stochastic frontier analysis
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