Yıl: 2023 Cilt: 80 Sayı: 3 Sayfa Aralığı: 329 - 344 Metin Dili: İngilizce DOI: 10.5505/TurkHijyen.2023.34793 İndeks Tarihi: 28-09-2023

Regional performance evaluation of healthcare services in Türkiye with cross-efficiency approach

Öz:
Objective: Monitoring and comparing regional service performance is inevitable to use resources effectively and allocate resources without creating interregional inequalities. With the Health Transformation Program, there has been an increase in the amount and quality of the service provided throughout the country. In addition, there has been a decrease in the inequalities between regions due to its implementation. In order to ensure continuity in health service delivery, health service activities in provinces and regions should be followed regularly, and their performance should be evaluated. Methods: The study calculated Data Envelopment Analysis efficiencies and Cross-efficiency for 26 subregions in the NUTS-II classification in Turkey. The number of hospital beds and primary care units were the inputs of the research; the number of primary, secondary and tertiary care visits and the number of inpatients are also the output of the study. Subregions are reordered and grouped according to their cross-efficiency. Decision-making units with unusual production structures were investigated by calculating Maverick Index scores. Results: Regarding DEA efficiency, the average efficiency score of the ten subregions classified as efficient is 0.97 (sd. 0.0395). The average cross-efficiency score obtained from the benevolent method is 0.89 (sd. 0.058), while the average score achieved through the aggressive method is 0.86 (sd. 0.574). Maverick index scores had a balanced distribution. The average maverick index score for aggressive and benevolent are 0.13 (std. 0.0474), and 0.0871 (std. 0.0420), respectively. Although only the Malatya subregion was classified as efficient in the traditional DEA method, it had low scores in the cross-efficiency evaluation and was classified as a maverick decision unit with an MI score of 0.21. Conclusion: The cross-efficiency method yields more valuable results because it employs more realistic weights for peer evaluation. These outcomes are simpler for decision-makers to comprehend and assess. The regions of Kocaeli, Adana, Aydın, Hatay, Şanlıurfa, and Tekirdağ exhibit notable performance in the cross-efficiency examination, as they have attained the most significant efficiency scores. The regions of Kastamonu, Erzurum, Van, and Ağrı exhibit poor levels of efficiency, thereby requiring the implementation of precautionary measures.
Anahtar Kelime: Cross-efficiency data envelopment analysis health policy performance management

Çapraz etkinlik yaklaşımı ile Türkiye’deki bölgesel sağlık hizmetleri performansının değerlendirilmesi

Öz:
Amaç: Kaynakların etkin kullanılması ve kaynak tahsisinin bölgeler arası eşitsizlikler yaratmaksızın yapılabilmesi için bölgesel hizmet performansının izlenmesi ve karşılaştırılması kaçınılmazdır. Sağlıkta Dönüşüm Programı ile ülke genlinde sunulan hizmetin miktar ve kalitesinde artışlar olmuştur. Ayrıca hizmet sunumundan kaynaklı bölgeler arası eşitsizliklerde de azalmalar olmuştur. Sağlık hizmetlerinde sürekliliğin sağlanması için illerde ve bölgelerdeki sağlık hizmetlerinin düzenli bir şekilde takip edilmesi ve performanslarının değerlendirilmesi gerekmektedir. Yöntem: Çalışmada Türkiye’deki IBBS-II sınıflamasında yer alan 26 alt bölgenin Veri Zarflama Analizi etkinlikleri ile Çapraz Etkinlikleri hesaplanmıştır. Hastane yatağı ve birinci basamak sağlık hizmet birimi sayıları araştırmanın girdilerini; birinci, ikinci ve üçüncü basamak sağlık tesislerindeki hasta muayene sayıları ile yatan hasta sayıları da çalışmanın çıktılarını oluşturmaktadır. Alt bölgeler çapraz etkinliklerine göre yeniden sıralanmış ve gruplandırılmıştır. Tüm alt bölgeler için Maverick İndeks puanları hesaplanarak sıra dışı üretim yapılarına sahip olan alt bölgeler belirlenmiştir. Bulgular: Veri zarflama analizi etkinlikleri bakımından on alt bölge etkin olarak gruplandırılmış olup bölgelerin ortalama etkinlik skoru 0,97’dir (sd. 0,0395 ). Benevolent yaklaşıma göre hesaplanan çapraz etkinlik ortalama skoru 0,89 (sd. 0,058) iken aggresive yaklaşıma göre hesaplanan ortalama skor ise 0,86’dır (sd. 0,0574). Maverick indeks skorları dengeli olarak dağılmıştır. Ortalama agressive maverick indeks skoru 0,13 (std. 0.0474) ve ortalama benevolent Maverick indeks skoru ise 0,0871’dir (std. 0.0420). Sadece Malatya alt bölgesi, geleneksel veri zarflama analizi yönteminde etkin olarak sınıflandırılmasına rağmen çapraz etkinlik değerlendirmesinde düşük skorlar almış ve 0,21 indeks skoru ile maverick karar birimi olarak sınıflandırılmıştır. Sonuç: Çapraz etkinlik yöntemi daha gerçekçi ağırlıklar kullanarak akran-değerlendirmesi yaptığı için daha kullanışlı sonuçlar ortaya koymaktadır. Söz konusu sonuçların karar alıcılar tarafından anlaşılması ve değerlendirilmesi daha kolaydır. Çapraz etkinlik değerlendirmesinde Kocaeli, Adana, Aydın, Hatay, Şanlıurfa ve Tekirdağ alt bölgeleri en yüksek etkinlik skorlu bölgeler olarak öne çıkmaktadır. Kastamonu, Erzurum, Van ve Ağrı alt bölgeleri de düşük etkinlik skorlu bölgeler olup önlem alınması gerekmektedir.
Anahtar Kelime: Çapraz etkinlik veri zarflama analizi sağlık politikası performans yönetimi

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
  • 1. Roberts MJ, Hsiao W, Berman P, Reich MR. Getting Health Reform Right. New York: Oxford University Press; 2004. 332 p.
  • 2. Goodwin N. National Health Systems: Overview. In: International Encyclopedia of Public Health. Elsevier; 2008. p. 497–512. Available from: http://linkinghub.elsevier.com/retrieve/pii/B9780123739605003117.
  • 3. WHO. The World Health Report Health Systems Financing - The path to universal coverage. Switzerland: WHO; 2010. Available from: https://apps.who.int/iris/bitstream/handle/10665/44371/9789241564021_eng.pdf?sequence=1&isAllowed=y.
  • 4. Şener M, Yiğit V. Sağlık sistemlerinin teknik verimliliği: OECD ülkeleri üzerinde bir araştırma. Süleyman Demirel Üniversitesi Sos Bilim Enst Derg, 2017;1(26):266–90.
  • 5. Atun R, Aydın S, Chakraborty S, Sümer S, Aran M, Gürol I, et al. Universal health coverage in Turkey: enhancement of equity. Lancet. 2013;382(9886):65–99. Available from: https://linkinghub.elsevier.com/retrieve/pii/S014067361361051X.
  • 6. Akdağ R. Türkiye Sağlıkta Dönüşüm Programı Değerlendirme Raporu (2003-2011). Ankara: T.C. Sağlık Bakanlığı; 2012. 444 p.
  • 7. OECD ve Dünya Bankası. OECD Sağlık Sistemi İncelemeleri: Türkiye. OECD; 2008. 128 p.
  • 8. The Ministry of Health of Türkiye. Health Statistics Yearbook 2021. The Minisitry of Health of Türkiye; 2021. 301 p.
  • 9. Nunamaker TR. Measuring routine nursing service efficiency: a comparison of cost per patient day and data envelopment analysis models. Health Serv Res. 1983;18(2 Pt 1):183–208.
  • 10. Sexton TR, Leiken AM, Sleeper S, Coburn AF. The Impact of prospective reimbursement on nursing home efficiency. Med Care. 1989;27(2):154–63. Available from: http://journals.lww.com/00005650-198902000-00006.
  • 11. Ozgen H, Ozcan YA. A national study of efficiency for dialysis centers: An examination of market competition and facility characteristics for production of multiple dialysis outputs. Health Serv Res. 2002;37(3):711–32.
  • 12. Lynch JR, Ozcan YA. Hospital closure: An efficiency analysis. Hosp Heal Serv Adm. 1994;39(2):205–20.
  • 13. Sherman HD. Hospital efficiency measurement and evaluation empirical test of a new technique. Med Care, 1984;22(10):922–38.
  • 14. Konca M. OECD ülkelerinin ulusal sağlık sistemlerinin zamana dayalı performansının değerlendirilmesi. Hacettepe Üni Derg; 2021.
  • 15. Hadad S, Hadad Y, Simon-Tuval T. Determinants of healthcare system’s efficiency in OECD countries. Eur J Heal Econ. 2013;14(2):253–65.
  • 16. Çayirtepe Z, Esatoğlu AE, Aral A, Kavuncubaşı Ş. Coronary Artery Bypass Graft Surgery Clinical Quality; A Network-DEA approach. Qeios. 2023;1–22.
  • 17. Pai CW, Ozcan YA, Jiang HJ. Regional variation in physician practice pattern: An examination of technical and cost efficiency for treating sinusitis. J Med Syst. 2000;24(2):103–17.
  • 18. Abolghasem S, Toloo M, Amézquita S. Cross-efficiency evaluation in the presence of flexible measures with an application to healthcare systems. Health Care Manag Sci [Internet]. 2019;22(3):512–33. Available from: http://link.springer.com/10.1007/s10729-019-09478-0.
  • 19. Flokou A, Kontodimopoulos N, Niakas D. Employing post-DEA Cross-evaluation and cluster analysis in a sample of Greek NHS Hospitals. J Med Syst [Internet]. 2011;35(5):1001–14. Available from: http://link.springer.com/10.1007/s10916-010-9533-9.
  • 20. Adejoh FO, Majahar M, Ismail MT. Data envelopment analysis cross-efficiency of primary health care in Lagos metropolis, Nigeria. Sci African [Internet]. 2022;17:e01336. Available from: https://doi.org/10.1016/j.sciaf.2022.e01336.
  • 21. Wang M. Economic performance evaluation of community health service centers: a DEA-based cross-efficiency study. Environ Sci Pollut Res [Internet]. 2022;30(7):18660–73. Available from: https://doi.org/10.1007/s11356-022-23048-y.
  • 22. Costantino N, Dotoli M, Epicoco N, Falagario M, Sciancalepore F. Using cross-efficiency fuzzy data envelopment analysis for healthcare facilities performance evaluation under uncertainty. Proc - 2013 IEEE Int Conf Syst Man, Cybern SMC 2013. 2013;912–7.
  • 23. Yaya S, Xi C, Xiaoyang Z, Meixia Z. Evaluating the efficiency of China’s healthcare service: A weighted DEA-game theory in a competitive environment. J Clean Prod [Internet]. 2020;270:122431. Available from: https://doi.org/10.1016/j.jclepro.2020.122431.
  • 24. Zare H, Tavana M, Mardani A, Masoudian S, Kamali Saraji M. A hybrid data envelopment analysis and game theory model for performance measurement in healthcare. Health Care Manag Sci [Internet]. 2019 Sep;22(3):475–88. Available from: http://link.springer.com/10.1007/s10729-018-9456-4.
  • 25. Torun N. Veri Zarflama Analizinin Bir Uzantısı Olarak Çapraz Etkinlik Değerlendirmesi. In: Kaçak H, editor. Sağlık Hizmetlerinde Performans Yönetimi. Ankara: Siyasal Kitabevi; 2023. p. 105–33.
  • 26. Kaçak H, Yıldırım S. Veri zarflama analizi çapraz etkinlik yaklaşımı ile eğitim ve araştırma hastaneleri performanslarının değerlendirilmesi. In: 3. Uluslararası Sağlık Bilimleri ve İnovasyon Kongresi. Ankara; 2021.
  • 27. Sherman HD, Zhu J. Service Productivity Management: Improving Service Performance Using Data Envelopment Analysis (DEA) . New York: Springer Science + Business Media; 2006. 343 p. Available from: http://ovidsp.ovid.com/ovidweb.cgi?T=JS&CSC=Y&NEWS=N&PAGE=fulltext&D=econ&AN=0889140
  • 28. Ozcan YA, Tone K. Health Care Benchmarking and Performance Evaluation An Assessment using Data Envelopment Analysis (DEA). International Series in Operations Research & Management Science. 2014. 329 p.
  • 29. Avkiran NK. Productivity Analysis in the Service Sector with Data Envelopment Analysis. Vol. 3, N K Avkiran. 2006. 423 p.
  • 30. Rowena J, Peter CS, Andrew S. Measuring Efficiency in Health Care Analytic Techniques and Health Policy. Cambridge University Press. New York: Cambridge University Press; 2006. 263 p.
  • 31. Wu J, Chu J, Sun J, Zhu Q, Liang L. Extended secondary goal models for weights selection in DEA cross-efficiency evaluation. Comput Ind Eng. 2016;93:143–51. Available from: http://dx.doi.org/10.1016/j.cie.2015.12.019.
  • 32. Charnes A, Cooper WW, Rhodes E. Measuring the efficiency of decision making units. Eur J Oper Res. 1978;2(6):429–44.
  • 33. Sexton TR, Silkman RH, Hogan AJ. Data envelopment analysis: Critique and extensions. New Dir Progr Eval. 1986;(32):73–105.
  • 34. Doyle J, Green R. Efficiency and cross-efficiency in DEA derivations, meanings and uses. J Oper Res Soc. 1994;45(5):567–78.
  • 35. Liu ST. A DEA ranking method based on cross-efficiency intervals and signal-to-noise ratio. Ann Oper Res. 2018;261(1–2):207–32.
  • 36. Hollingsworth B. The measurement of efficiency and productivity of health care delivery. Health Econ. 2008;17(10):1107–28. Available from: http://doi.wiley.com/10.1002/hec.1391.
  • 37. Zhu J. Quantitative Models for Performance Evaluation and Benchmarking. 2nd ed. Operations Research. Cham: Springer International Publishing; 2014. 420 p. (International Series in Operations Research & Management Science; vol. 213). Available from: http://link.springer.com/10.1007/978-3-319-06647-9.
  • 38. Cooper WW, Seiford LM, Zhu J, Lawrence M. Seiford, Zhu J, Hillier FS. Handbook on Data Envelopment Analysis. Cooper WW, Seiford LM, Zhu J, editors. Boston, MA: Springer US; 2011. 524 p. (International Series in Operations Research & Management Science; vol. 164). Available from: http://link.springer.com/10.1007/978-1-4419-6151-8.
  • 39. Ashkiani S. Mavericks Revisited: A New Index to Identify Maverick Units in Data Envelopment Analysis. 2019.
  • 40. Lim S. Context-dependent data envelopment analysis with cross-efficiency evaluation. J Oper Res Soc. 2012;63(1):38–46. Available from: https://www.tandfonline.com/doi/abs/10.1057/jors.2011.29.
  • 41. Pessanha JFM, Marinho A, Rezende SM, Laurencel L da C, Amaral MR dos S. DEA Cross-efficiency in the R program DEA Cross-efficiency in the R program. 2016.
  • 42. Lim S. Minimax and maximin formulations of cross-efficiency in DEA. Comput Ind Eng. 2012;62(3):726–31. Available from: http://dx.doi.org/10.1016/j.cie.2011.11.010.
  • 43. Kacak H. Effects of public health services on health systems performance: An application with dynamic network data envelopment analysis. Turk Hij Den Biyol Derg. 2022;79(3):531–48. Available from: https://jag.journalagent.com/z4/download_fulltext.asp?pdir=turkhijyen&plng=tur&un=THDBD-70194.
  • 44. Khushalani J, Ozcan YA. Are hospitals producing quality care efficiently? An analysis using Dynamic Network Data Envelopment Analysis (DEA). Socioecon Plann Sci. 2017;60:15–23. Available from: https://doi.org/10.1016/j.seps.2017.01.009.
  • 45. Tiemann O, Schreyögg J. Changes in hospital efficiency after privatization. Health Care Manag Sci. 2012;15(4):310–26.
  • 46. Chou T-H, Ozcan YA, White KR. Technical and scale efficiencies of Catholic hospitals: Does a system value of stewardship matter? In: Tànfani E, Testi A, editors. International Series in Operations Research and Management Science. Milano: Springer Milan; 2012. p. 83–101. (International Series in Operations Research & Management Science; vol. 173). Available from: http://www.springerlink.com/index/10.1007/978-88-470-2321-5_6.
  • 47. Zakowska I, Godycki-Cwirko M. Data envelopment analysis applications in primary health care: a systematic review. Fam Pract. 2019;20(20):1–7. Available from: https://academic.oup.com/fampra/advance-article/doi/10.1093/fampra/cmz057/5586691.
  • 48. Carrillo M, Jorge JM. Integrated approach for computing aggregation weights in cross-efficiency evaluation. Oper Res Perspect. 2018;5:256–64. Available from: https://linkinghub.elsevier.com/retrieve/pii/S2214716018301234.
  • 49. Örkcü HH, Özsoy VS, Örkcü M, Bal H. A neutral cross efficiency approach for basic two stage production systems. Expert Syst Appl. 2019;125:333–44. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0957417419300582.
  • 50. Wang Y-M, Chin K-S. A neutral DEA model for cross-efficiency evaluation and its extension. Expert Syst Appl. 2010;37(5):3666–75. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0957417409008902.
APA KAÇAK H (2023). Regional performance evaluation of healthcare services in Türkiye with cross-efficiency approach. , 329 - 344. 10.5505/TurkHijyen.2023.34793
Chicago KAÇAK Hakan Regional performance evaluation of healthcare services in Türkiye with cross-efficiency approach. (2023): 329 - 344. 10.5505/TurkHijyen.2023.34793
MLA KAÇAK Hakan Regional performance evaluation of healthcare services in Türkiye with cross-efficiency approach. , 2023, ss.329 - 344. 10.5505/TurkHijyen.2023.34793
AMA KAÇAK H Regional performance evaluation of healthcare services in Türkiye with cross-efficiency approach. . 2023; 329 - 344. 10.5505/TurkHijyen.2023.34793
Vancouver KAÇAK H Regional performance evaluation of healthcare services in Türkiye with cross-efficiency approach. . 2023; 329 - 344. 10.5505/TurkHijyen.2023.34793
IEEE KAÇAK H "Regional performance evaluation of healthcare services in Türkiye with cross-efficiency approach." , ss.329 - 344, 2023. 10.5505/TurkHijyen.2023.34793
ISNAD KAÇAK, Hakan. "Regional performance evaluation of healthcare services in Türkiye with cross-efficiency approach". (2023), 329-344. https://doi.org/10.5505/TurkHijyen.2023.34793
APA KAÇAK H (2023). Regional performance evaluation of healthcare services in Türkiye with cross-efficiency approach. Türk Hijyen ve Deneysel Biyoloji Dergisi, 80(3), 329 - 344. 10.5505/TurkHijyen.2023.34793
Chicago KAÇAK Hakan Regional performance evaluation of healthcare services in Türkiye with cross-efficiency approach. Türk Hijyen ve Deneysel Biyoloji Dergisi 80, no.3 (2023): 329 - 344. 10.5505/TurkHijyen.2023.34793
MLA KAÇAK Hakan Regional performance evaluation of healthcare services in Türkiye with cross-efficiency approach. Türk Hijyen ve Deneysel Biyoloji Dergisi, vol.80, no.3, 2023, ss.329 - 344. 10.5505/TurkHijyen.2023.34793
AMA KAÇAK H Regional performance evaluation of healthcare services in Türkiye with cross-efficiency approach. Türk Hijyen ve Deneysel Biyoloji Dergisi. 2023; 80(3): 329 - 344. 10.5505/TurkHijyen.2023.34793
Vancouver KAÇAK H Regional performance evaluation of healthcare services in Türkiye with cross-efficiency approach. Türk Hijyen ve Deneysel Biyoloji Dergisi. 2023; 80(3): 329 - 344. 10.5505/TurkHijyen.2023.34793
IEEE KAÇAK H "Regional performance evaluation of healthcare services in Türkiye with cross-efficiency approach." Türk Hijyen ve Deneysel Biyoloji Dergisi, 80, ss.329 - 344, 2023. 10.5505/TurkHijyen.2023.34793
ISNAD KAÇAK, Hakan. "Regional performance evaluation of healthcare services in Türkiye with cross-efficiency approach". Türk Hijyen ve Deneysel Biyoloji Dergisi 80/3 (2023), 329-344. https://doi.org/10.5505/TurkHijyen.2023.34793