Fuzzy Efficiency Estimates of the Turkish Health System: A Comparison of Interval, Bias-Corrected, and Fuzzy Data Envelopment Analysis
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS, cilt.1, sa.1, ss.1-24, 2023 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 1 Sayı: 1
- Basım Tarihi: 2023
- Doi Numarası: 10.1007/s40815-023-01519-9
- Dergi Adı: INTERNATIONAL JOURNAL OF FUZZY SYSTEMS
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Civil Engineering Abstracts
- Sayfa Sayıları: ss.1-24
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- Hacettepe Üniversitesi Adresli: Evet
Özet
This study applies a multistep fuzzy stochastic procedure to evaluate Turkish health system efficiency by comparing crisp and stochastic efficiency estimates blending machine learning predictors. Conventional, bias-corrected, and fuzzy data envelopment analysis (DEA) estimates are employed and compared to explore province-based health systems’ efficiency scores. Fuzzy DEA α-level models are used to assess underlying uncertainty, yielding fuzzy results by changing 10 different alpha (α)-cut parameters from 0.10 to 1. Data are obtained from the official statistics of the Turkish Statistical Institute, and cross-province efficiency comparisons are performed through spatial analysis of the best and worst performers. A Pythagorean forest is constructed incorporating random forest regression to identify the most accurate predictors of province-based efficiency scores. The results reveal that bias correction and fuzziness outperform conventional efficiency analysis. High efficiency scores are observed when the α-cut parameter in the fuzzy DEA application is increased. High correlations are observed between efficiency scores elicited from crisp and stochastic DEA estimates (