Araştırma Makalesi
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Dynamic Network DEA Approach for Evaluating Hospital Performance in the Healthcare Sector

Yıl 2025, Cilt: 37 Sayı: 3, 252 - 262, 24.09.2025
https://doi.org/10.7240/jeps.1583402

Öz

This study simultaneously evaluates the performance of Training and Research Hospitals in Turkey for 2018 and 2019 at both the overall and sub-unit levels. Traditional Data Envelopment Analysis (DEA) models treat decision-making units as a single process, often neglecting internal structures. To overcome this limitation, the study employs the Dynamic Network Data Envelopment Analysis (DN-DEA) approach, which incorporates two interrelated sub-units: administrative services and medical care services. This enables independent evaluation of sub-units, without disregarding their mutual connections. The results show that hospitals efficient in both sub-units are classified as fully efficient. However, some hospitals not on the overall efficiency frontier demonstrated full efficiency in specific sub-units. For instance, H12 and H33 were efficient in administrative services, while hospitals such as H14, H17, H21, and H23 attained efficiency only in medical services. In 2018, the budget account balance was identified as the most critical input requiring reduction (72.7%) for inefficient hospitals, followed by the number of resident physicians (50.7%). In 2019, the budget balance remained the top priority for reduction (62.1%), while the insufficient reduction in resident physicians caused the required adjustment to rise to 52.3%. In light of these findings, it is recommended that hospital performance management consider not only overall efficiency scores but also sub-unit-level analyses. Furthermore, the study emphasizes that improvements in budget management and human resource planning may play a critical role in enhancing hospital efficiency.

Teşekkür

This study is an expanded version of the paper presented actively at the “V. International Applied Statistics Congress (UYIK-2024)” held in Istanbul /Türkiye on May 21-23, 2024.

Kaynakça

  • Aletras V., Kontodimopoulos N., Athanasios Z., Dimitris N. (2007). The short-term effect on technical and scale efficiency of establishing regional health systems and general management in Greek NHS hospitals, Health Policy, Volume 83, Issues 2–3, 236-245.
  • Chen, S.N. (2006). Productivity changes in Taiwanese hospitals and the national health insurance. The Service Industries Journal, 26(4), 459-477.
  • Ravaghi, H., Afshari, M., Isfahani, P., & Bélorgeot, V.D. (2019). A systematic review on hospital inefficiency in the Eastern Mediterranean Region: Sources and solutions. BMC Health Services Research, 19(1), 830.
  • Banker, R.D., Charnes, A., & Cooper, W.W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078-1092.
  • Farrell, M.J. (1957). The measurement of productive efficiency, Journal of the Royal Statistical Series A (General), 120(3), 253-290.
  • Kawaguchi, H., Tone, K., & Tsutsui, M. (2014). Estimation of the efficiency of Japanese hospitals using a dynamic and network data envelopment analysis model. Health Care Management science 17(2), 101–112.
  • Charnes A., Cooper W.W., & Rhodes E. (1978) Measuring the efficiency of decision-making units, European Journal of Operational Research, 2(6), 429–444.
  • Färe, R. and Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, 34, 35–49.
  • Löthgren, M., Tambour, M., (1999) Productivity and customer satisfaction in Swedish pharmacies: a DEA network model. European Journal of Operations Research, 115, 449–458.
  • Golany, B., Hackman, S. T., & Passy, U. (2006). An efficiency measurement framework for multi-stage production systems. Annals of Operations Research, 145, 51–68.
  • Yu, M. M., & Lee, B. C.Y. (2009). Efficiency and effectiveness of service business: Evidence from international tourist hotels in Taiwan. Tourism Management, 30, 571–580.
  • Fukuyama H., Weber, W.L. (2010) A slacks-based inefficiency measure for a two-stage system with bad outputs. Omega 38(5), 239–410
  • Kao, C., & Hwang, S.N. (2008). Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. European Journal of Operational Research, 185, 418–429.
  • Yu, M. M., & Fan, C. K. (2009). Measuring the performance of multimode bus transit: A mixed structure network DEA model. Transportation Research Part E-Logistics and Transportation Review, Elsevier, 45(3), 501–515.
  • Lobo, M.S.C., Rodrigues, H.C., André, E.C.G., Azeredo, J.A., & Lins, M.P.E. (2016). Dynamic network data envelopment analysis for university hospital evaluation. Rev Saude Publica, 50:22.
  • Färe, R., & Grosskopf, S. (1996). Intertemporal production frontiers: with dynamic DEA. Journal of the Operational Research Society, 48(6), 656-659.
  • Emrouznejad, A. veThanassoulis, E., (2005) A mathematical model for dynamic efficiency using data envelopment analysis. Applied Mathematics and Computation 160(2), 363–378.
  • Tone, K., & Tsutsui, M. (2014). Dynamic DEA with network structure: A slacks- based measure approach. Omega, 42, 124–131.
  • Sherman, H.D. (1984). Hospital efficiency measurement and evaluation, Medical Care Research and Review, 22(10), 922-939.
  • Sikka, V., Luke, R.D. & Ozcan, Y.A., (2009). The efficiency of hospital-based clusters: evaluating system performance using data envelopment analysis, Health Care Management Review, 34(3), 251-261.
  • Spinks, J., & Hollingsworth, B. (2009). Cross-country comparisons of technical efficiency of health production: A demonstration of pitfalls, Applied Economics, 41, 417–427.
  • Du, J., Wang, J., Chen, Y., Chou, S.Y., & Zhu, J. (2014). Incorporating health outcomes in Pennsylvania hospital efficiency: an additive super-efficiency DEA approach, Annual Operation Research, 221, 161–172.
  • Harrison, J. P., Coppola, M. N., & Wakefield, M. (2004). Efficiency of federal hospitals in the United States. Journal of Medical Systems, 28(5), 411–422.
  • Tsai, P.F., & Mar Molinero C. (2002). A variable returns to scale data envelopment analysis model for the joint determination of efficiencies with an example of the UK health service. European Journal of Operational Research, 141, 21–38.
  • Khushalani, Y., & Ozcan Y.A. (2017). Are hospitals producing quality care efficiently? An analysis using dynamic network data envelopment analysis (DEA), Socio-Economic Planning Sciences, 60, 5-23.
  • O’Neil, L., Marıon, R., Heıdenberger, K., & Kraus, M. (2008). A cross-national comparison and taxonomy of DEA-based hospital efficiency studies, Socio-Economic Planning Sciences, 42, 158–189.

Dinamik Ağ VZA Yaklaşımıyla Sağlık Sektöründe Hastane Performans Değerlendirmesi

Yıl 2025, Cilt: 37 Sayı: 3, 252 - 262, 24.09.2025
https://doi.org/10.7240/jeps.1583402

Öz

Karar birimlerini tüm girdileri toplayan ve bunları çıktılara dönüştüren tek bir süreçten oluşan bir yapı olarak gören geleneksel Veri Zarflama Analizi (VZA) modelleri organizasyonu bir kara kutu olarak görür ve iç yapısının ilişkilerini ihmal etmektedir. Ağ VZA modeli, hastanelerin toplam etkinliğini değerlendirmekle kalmaz aynı zamanda ara değişkenler ile birbirine bağlı iç organizasyonlar arası bağlantıları da analiz ederek her bir alt faaliyet biriminin etkinliklerini ölçme imkanı sunmaktadır. Hastaneler idari birimler ve tedavi bakım hizmetleri olmak üzere iki alt birimden oluşmaktadır. Birimlerden birinin çıktı öğesi, diğerinin girdisi olarak birbirleriyle bağlantılı olarak faaliyetlerini sürdürür. Bu çalışma, Dinamik Ağ Veri Zarflama Analizi (DN-VZA) modeli kullanılarak Türkiye Eğitim araştırma hastanelerinin performansını ölçmeyi amaçlamaktadır. Bir dönemden diğerine aktarılan unsurların analize dahil edilmesi ve zamana bağlı etkinlik değişiminin araştırılması için Dinamik VZA (DN DEA) yaklaşımı tercih edilmiştir. Sağlık sektörüne ilişkin çıktılara müdahale etmek daha zor ve uzun süreç gerektirdiğinden hastanelerin etkinlik ölçümünde girdi odaklı VZA kullanmak uygulanabilir sonuçlar vermektedir. Mevcut kaynaklarla girdilerin azaltılabilirliğini ölçmek, maksimum etkinliği sağlayacaktır. Büyük ve küçük ölçekli hastane işletmelerini değerlendirirken ölçeğe gore değişken getiri yaklaşımını kullanmak daha doğru değerlendirme olacağından VZA’nın VRS (değişken getiri) modeli kullanılmıştır. DN modelin çözümünde Gevşek tabanlı ölçüm yaklaşımı uygulanmıştır. DN DEA model analizi sonuçlarına göre, birbirine görece etkin olmayan hastaneler belirlenmiş ve verimliliği artırabilecek olası iyileştirmeler sunulmuştur.

Kaynakça

  • Aletras V., Kontodimopoulos N., Athanasios Z., Dimitris N. (2007). The short-term effect on technical and scale efficiency of establishing regional health systems and general management in Greek NHS hospitals, Health Policy, Volume 83, Issues 2–3, 236-245.
  • Chen, S.N. (2006). Productivity changes in Taiwanese hospitals and the national health insurance. The Service Industries Journal, 26(4), 459-477.
  • Ravaghi, H., Afshari, M., Isfahani, P., & Bélorgeot, V.D. (2019). A systematic review on hospital inefficiency in the Eastern Mediterranean Region: Sources and solutions. BMC Health Services Research, 19(1), 830.
  • Banker, R.D., Charnes, A., & Cooper, W.W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078-1092.
  • Farrell, M.J. (1957). The measurement of productive efficiency, Journal of the Royal Statistical Series A (General), 120(3), 253-290.
  • Kawaguchi, H., Tone, K., & Tsutsui, M. (2014). Estimation of the efficiency of Japanese hospitals using a dynamic and network data envelopment analysis model. Health Care Management science 17(2), 101–112.
  • Charnes A., Cooper W.W., & Rhodes E. (1978) Measuring the efficiency of decision-making units, European Journal of Operational Research, 2(6), 429–444.
  • Färe, R. and Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, 34, 35–49.
  • Löthgren, M., Tambour, M., (1999) Productivity and customer satisfaction in Swedish pharmacies: a DEA network model. European Journal of Operations Research, 115, 449–458.
  • Golany, B., Hackman, S. T., & Passy, U. (2006). An efficiency measurement framework for multi-stage production systems. Annals of Operations Research, 145, 51–68.
  • Yu, M. M., & Lee, B. C.Y. (2009). Efficiency and effectiveness of service business: Evidence from international tourist hotels in Taiwan. Tourism Management, 30, 571–580.
  • Fukuyama H., Weber, W.L. (2010) A slacks-based inefficiency measure for a two-stage system with bad outputs. Omega 38(5), 239–410
  • Kao, C., & Hwang, S.N. (2008). Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. European Journal of Operational Research, 185, 418–429.
  • Yu, M. M., & Fan, C. K. (2009). Measuring the performance of multimode bus transit: A mixed structure network DEA model. Transportation Research Part E-Logistics and Transportation Review, Elsevier, 45(3), 501–515.
  • Lobo, M.S.C., Rodrigues, H.C., André, E.C.G., Azeredo, J.A., & Lins, M.P.E. (2016). Dynamic network data envelopment analysis for university hospital evaluation. Rev Saude Publica, 50:22.
  • Färe, R., & Grosskopf, S. (1996). Intertemporal production frontiers: with dynamic DEA. Journal of the Operational Research Society, 48(6), 656-659.
  • Emrouznejad, A. veThanassoulis, E., (2005) A mathematical model for dynamic efficiency using data envelopment analysis. Applied Mathematics and Computation 160(2), 363–378.
  • Tone, K., & Tsutsui, M. (2014). Dynamic DEA with network structure: A slacks- based measure approach. Omega, 42, 124–131.
  • Sherman, H.D. (1984). Hospital efficiency measurement and evaluation, Medical Care Research and Review, 22(10), 922-939.
  • Sikka, V., Luke, R.D. & Ozcan, Y.A., (2009). The efficiency of hospital-based clusters: evaluating system performance using data envelopment analysis, Health Care Management Review, 34(3), 251-261.
  • Spinks, J., & Hollingsworth, B. (2009). Cross-country comparisons of technical efficiency of health production: A demonstration of pitfalls, Applied Economics, 41, 417–427.
  • Du, J., Wang, J., Chen, Y., Chou, S.Y., & Zhu, J. (2014). Incorporating health outcomes in Pennsylvania hospital efficiency: an additive super-efficiency DEA approach, Annual Operation Research, 221, 161–172.
  • Harrison, J. P., Coppola, M. N., & Wakefield, M. (2004). Efficiency of federal hospitals in the United States. Journal of Medical Systems, 28(5), 411–422.
  • Tsai, P.F., & Mar Molinero C. (2002). A variable returns to scale data envelopment analysis model for the joint determination of efficiencies with an example of the UK health service. European Journal of Operational Research, 141, 21–38.
  • Khushalani, Y., & Ozcan Y.A. (2017). Are hospitals producing quality care efficiently? An analysis using dynamic network data envelopment analysis (DEA), Socio-Economic Planning Sciences, 60, 5-23.
  • O’Neil, L., Marıon, R., Heıdenberger, K., & Kraus, M. (2008). A cross-national comparison and taxonomy of DEA-based hospital efficiency studies, Socio-Economic Planning Sciences, 42, 158–189.
Toplam 26 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Nicel Karar Yöntemleri
Bölüm Araştırma Makaleleri
Yazarlar

Eda Koçak 0000-0001-7094-2587

Fazıl Gökgöz 0000-0002-9228-7699

Erken Görünüm Tarihi 15 Eylül 2025
Yayımlanma Tarihi 24 Eylül 2025
Gönderilme Tarihi 12 Kasım 2024
Kabul Tarihi 21 Temmuz 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 37 Sayı: 3

Kaynak Göster

APA Koçak, E., & Gökgöz, F. (2025). Dynamic Network DEA Approach for Evaluating Hospital Performance in the Healthcare Sector. International Journal of Advances in Engineering and Pure Sciences, 37(3), 252-262. https://doi.org/10.7240/jeps.1583402
AMA Koçak E, Gökgöz F. Dynamic Network DEA Approach for Evaluating Hospital Performance in the Healthcare Sector. JEPS. Eylül 2025;37(3):252-262. doi:10.7240/jeps.1583402
Chicago Koçak, Eda, ve Fazıl Gökgöz. “Dynamic Network DEA Approach for Evaluating Hospital Performance in the Healthcare Sector”. International Journal of Advances in Engineering and Pure Sciences 37, sy. 3 (Eylül 2025): 252-62. https://doi.org/10.7240/jeps.1583402.
EndNote Koçak E, Gökgöz F (01 Eylül 2025) Dynamic Network DEA Approach for Evaluating Hospital Performance in the Healthcare Sector. International Journal of Advances in Engineering and Pure Sciences 37 3 252–262.
IEEE E. Koçak ve F. Gökgöz, “Dynamic Network DEA Approach for Evaluating Hospital Performance in the Healthcare Sector”, JEPS, c. 37, sy. 3, ss. 252–262, 2025, doi: 10.7240/jeps.1583402.
ISNAD Koçak, Eda - Gökgöz, Fazıl. “Dynamic Network DEA Approach for Evaluating Hospital Performance in the Healthcare Sector”. International Journal of Advances in Engineering and Pure Sciences 37/3 (Eylül2025), 252-262. https://doi.org/10.7240/jeps.1583402.
JAMA Koçak E, Gökgöz F. Dynamic Network DEA Approach for Evaluating Hospital Performance in the Healthcare Sector. JEPS. 2025;37:252–262.
MLA Koçak, Eda ve Fazıl Gökgöz. “Dynamic Network DEA Approach for Evaluating Hospital Performance in the Healthcare Sector”. International Journal of Advances in Engineering and Pure Sciences, c. 37, sy. 3, 2025, ss. 252-6, doi:10.7240/jeps.1583402.
Vancouver Koçak E, Gökgöz F. Dynamic Network DEA Approach for Evaluating Hospital Performance in the Healthcare Sector. JEPS. 2025;37(3):252-6.