Araştırma Makalesi
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Türkiye’de Tıbbi Görüntüleme Cihazlarının Verimliliğini Etkileyen Faktörlerin Değerlendirilmesi: İki Aşamalı Veri Zarflama Analizi

Yıl 2022, Cilt: 9 Sayı: 4, 492 - 500, 30.12.2022
https://doi.org/10.34087/cbusbed.994765

Öz

Giriş ve Amaç: Sağlık sektörü hem emek hem de teknoloji yoğun bir sektör olarak görülmektedir. Özellikle teknolojik gelişmelere bağlı olarak sağlık harcamalarında büyük artışlar yaşanmaktadır. Sağlık harcamaları üzerinde baskı oluşturan teknolojik cihazlar tüm dünyayı etkilerken, teknoloji yönetimlerini küresel bir endişe ve uzun vadeli bir sorun haline getiriyor. Bu çalışma, Türkiye'de 81 il arasında tıbbi cihaz kullanım etkinliğini karşılaştırmayı amaçlamaktadır. Bu çalışmanın temel amacı, tıbbi cihaz kullanımı açısından verimli ve verimsiz illeri yansıtarak ulusal bir çerçeve belirlemek ve buna göre çeşitli önerilerde bulunmaktır.
Gereç ve Yöntemler: Çalışma iki aşamalı analizden oluşmaktadır. İlk olarak Veri Zarflama Analizi (DEA) ve ardından Sıradan En Küçük Kareler (OLS) analizi yapılmıştır. DEA ile tıbbi cihaz kullanımında verimli ve verimsiz iller belirlenirken, illerin verimliliğini etkileyen faktörler OLS ile belirlenmiştir.
Bulgular: Toplam 81 ilden 22'si verimli, 59'u verimsiz bulunmuştur. Regresyon modeline göre büyükşehir olma durumu, üniversite mezun oranı ve kişi başına düşen gayri safi yurtiçi hasıla değişkenlerinin verimlilik skoru üzerinde istatistiksel olarak anlamlı bir etkisi bulunamazken (p>0.05); hekim sayısı ve yaşlı bağımlılık oranının verimlilik skoru üzerinde istatistiksel olarak anlamlı bir etkiye sahip olduğu görülmüştür (p≤0.05).
Sonuç: Bu çalışmadan elde edilen sonuçların sağlık politikası yapıcılarına ve planlayıcılarına yol gösterici bilgiler sağlayacağı düşünülmektedir.

Kaynakça

  • Jonsson, E, Banta, D, Management of health technologies: an international view, BMJ: British Medical Journal, 1999, 319, 1293-1295.
  • World Health Organization, Health technology assessment of medical devices, Geneva: World Health Organization, 2011.
  • Green, A, Bennett, S, Sound choices: enhancing capacity for evidence-informed health policy, Geneva: Alliance for Health Policy and Systems Research & World Health Organization, 2007.
  • Altman, S.H, Blendon, R, Medical technology: the culprit behind health care costs? Proceedings of the 1977 Sun Valley Forum on National Health, 1977.
  • Banta, D, Health care technology as a policy issue. In: Banta D, Battista R, Gelband H, Jonsson E (eds) Health care technology and its assessment in eight countries. Washington, DC: United States Congress, 1995, 275-334.
  • Mohandas, A, Foley, K.A, Medical devices: adapting to the comparative effectiveness landscape, Biotechnology Healthcare, 2010, 7(2), 25-28.
  • Charnes, A, Cooper, W, Rhodes, E, Measuring the efficiency of decision making, European Journal of Operational Research, 1978, 2(6), 429–44.
  • Farrell, M.J, The measurement of productive efficiency, Journal of the Royal Statistical Society Series A (General) 1957, 120(3), 253-290.
  • Banker, R.D, Charnes, A, Cooper, W.W, Some models for estimating technical and scale ınefficiencies in data envelopment analysis, Management Science, 1984, 30(9), 1078-1092.
  • Charnes, A, Cooper, W, Rhodes, E, Evaluating program and managerial efficiency: An application of data envelopment analysis to program follow through, Management Science, 1981, 27(6), 668-697.
  • Chern, J.Y, Wan, T.T, The impact of the prospective payment system on the technical efficiency of hospitals, Journal of Medical Systems, 2000, 24, 159–172.
  • Sherman, H, Zhu, J, Service productivity management: Improving service performance using data envelopment analysis (DEA), Springer, USA, 2006.
  • Ozcan, Y.A, Health care benchmarking and performance evaluation, International Series in Operations Research & Management Science, Springer, USA, 2014.
  • Stöckl, D, Dewitte, K, Thienpont, L.M, Validity of linear regression in method comparison studies: Is it limited by the statistical model or the quality of the analytical input data? Clinical Chemistry, 1998, 44, 2340-2346.
  • Doi, K, Diagnostic imaging over the last 50 years: Research and development in medical imaging science and technology, Physics in Medicine & Biology, 2006, 51(13), R5-R27.
  • Quaday, K.A, Salzman, J.G, Gordon, B.D, Magnetic resonance imaging and computed tomography utilization trends in an academic ED, The American Journal of Emergency Medicine, 2014, 32(6), 524-528.
  • Semin, S, Demiral, Y, Dicle, O, Trends in diagnostic imaging utilization in a university hospital in Turkey, International Journal of Technology Assessment in Health Care, 2006, 22(4), 532-536.
  • Smith-Bindman, R, Miglioretti, D.L, Larson, E.B, Rising use of diagnostic medical imaging in a large integrated health system, Health Affairs, 2008, 27(6), 1491-1502.
  • Wang, L, Nie, J.X, Tracy, C.S, Moineddin, R, Upshur, R.E, Utilization patterns of diagnostic imaging across the late life course: a population-based study in Ontario, Canada, International Journal of Technology Assessment in Health Care, 2008, 24(4), 384-390.
  • Hillman, B.J, Goldsmith, J.C, The uncritical use of high-tech medical imaging, New England Journal of Medicine, 2010, 363(1), 4-6.
  • Hendee, W.R, Becker, G.J, Borgstede, J.P, Bosma, J, Casarella, W.J, Erickson, B.A, Maynard, C.D, Thrall, J.H, Wallner, P.E, Addressing overutilization in medical imaging, Radiology, 2010, 257(1), 240-245.
  • Lang, K, Huang, H, Lee, D.W, Federico, V, Menzin, J, National trends in advanced outpatient diagnostic imaging utilization: an analysis of the medical expenditure panel survey, 2000-2009, BMC Medical Imaging, 2013, 13(40), 1-10.
  • Weilburg, J.B, Sistrom, C.L, Rosenthal, D.I, Stout, M.B, Dreyer, K.J, Rockett, H.R, et al., Utilization management of high-cost imaging in an outpatient setting in a large stable patient and provider cohort over 7 years, Radiology, 2017, 284(3), 766-776.
  • Iglehart, J.K, Health insurers and medical-imaging policy—a work in progress, New England Journal of Medicine, 2009, 360, 1030-1037.
  • Baker, L, Birnbaum, H, Geppert, J, Mishol, D, Moyneur, E, The relationship between technology availability and health care spending: Attempts to address technology availability and rising costs could end up badly misguided if implications for quality are not considered, Health Affairs, 2003, 22(Suppl1), W3-537.
  • Maitino, A.J, Levin, D.C, Parker, L, Rao, V.M, Sunshine, J.H, Practice patterns of radiologists and nonradiologists in utilization of noninvasive diagnostic imaging among the Medicare population 1993–1999, Radiology, 2003, 228(3), 795-801.
  • Beinfeld, M.T, Gazelle, G.S, Diagnostic imaging costs: are they driving up the costs of hospital care? Radiology, 2005, 235(3), 934-939.
  • OECD. Computed tomography (CT) exams (indicator). doi: 10.1787/3c994537-en. Accessed 24 January 2019.
  • Okrah, K, Vaughan‐Sarrazin, M, Kaboli, P, Cram, P, Echocardiogram utilization among rural and urban veterans, The Journal of Rural Health, 2012, 28(2), 211-220.
  • Goode, A.P, Freburger, J.K, Carey, T.S, The influence of rural versus urban residence on utilization and receipt of care for chronic low back pain, The Journal of Rural Health, 2013, 29(2), 205-214.
  • Onega, T, Hubbard, R, Hill, D, Lee, C.I, Haas, J.S, Carlos, H.A, et al., Geographic access to breast imaging for US women, Journal of the American College of Radiology, 2014, 11(9), 874-882.
  • Cinaroglu, S, Baser, O, Spatial distribution of total number of medical devices in Turkey: A classification analysis, International Journal of Medicine and Public Health, 2017, 7(2), 102-106.
  • Sonğur, C, Top, M, Regional clustering of medical imaging Technologies, Computers in Human Behavior, 2016, 61, 333-343.
  • Ozcan, Y.A, Legg, J.S, Performance measurement for radiology providers: a national study, International Journal of Healthcare Technology and Management, 2014, 14(3), 209-221.
  • Keshtkaran, A, Barouni, M, Ravangard, R, Yandrani, M, Economic efficiency of radiology wards using data envelopment analysis: Case study of Iran, Health, 2014, 6(5), 311-316.
  • Hillman, B.J, Joseph, C.A, Mabry, M.R, Sunshine, J.H, Kennedy, S.D, Noether, M, Frequency and costs of diagnostic imaging in office practice—a comparison of self-referring and radiologist-referring physicians, New England Journal of Medicine, 1990, 323(23), 1604-1608.
  • Hillman, B.J, Olson, G.T, Griffith, P.E, Sunshine, J.H, Joseph, C.A, Kennedy, S.D, etal., Physicians' utilization and charges for outpatient diagnostic imaging in a Medicare population, Journal of the American Medical Association (JAMA), 1992, 268(15), 2050-2054.
  • Cherkin, D.C, Deyo, R.A, Wheeler, K, Ciol, M.A, Physician variation in diagnostic testing for low back pain: Who you see is what you get, Arthritis & Rheumatism: Official Journal of the American College of Rheumatology, 1994, 37(1), 15-22.
  • Rosen, M.P, Davis, R.B, Lesky, L.G, Utilization of outpatient diagnostic imaging: Does the physician’s gender play a role? Journal of General Internal Medicine, 1997, 12(7), 407-411.
  • Kanzaria, H.K, Hoffman, J.R, Probst, M.A, Caloyeras, J.P, Berry, S.H, Brook, R.H, Emergency physician perceptions of medically unnecessary advanced diagnostic imaging, Academic Emergency Medicine, 2015, 22(4), 390-398.
  • Sistrom, C, McKay, N.L, Weilburg, J.B, Atlas, S.J, Ferris, T.G, Determinants of diagnostic imaging utilization in primary care, The American Journal of Managed Care, 2012, 18(4), e135-e144.
  • Levin, D.C, Rao, V.M, Factors that will determine future utilization trends in diagnostic imaging, Journal of the American College of Radiology, 2016, 13(8), 904-908.
  • Toms, A.P, Cash, C.J, Linton, S.J, Dixon, A.K, Requests for body computed tomography: increasing workload, increasing indications and increasing age, European Radiology, 2001, 11(12), 2633-2637.
  • Latham, L.P, Ackroyd-Stolarz, S, Emergency department utilization by older adults: A descriptive study, Canadian Geriatrics Journal, 2014, 17(4), 118-125.
  • Lysdahl, K.B, Hofmann, B.M, What causes increasing and unnecessary use of radiological investigations? A survey of radiologists' perceptions, BMC Health Services Research, 2009, 9(1), 155-163.
  • Hu, M, A study on medical imaging equipment productivity and utilization. Proceedings of the 2011 Industrial Engineering Research Conference, 2011, 1-8.

Evaluation of the factors that affect the efficiency of diagnostic imaging technologies in Turkey: A two-stage data envelopment analysis

Yıl 2022, Cilt: 9 Sayı: 4, 492 - 500, 30.12.2022
https://doi.org/10.34087/cbusbed.994765

Öz

Objective: The healthcare sector is observed to be both labor and technology concentrated. Particularly based on the technological developments, there is a major increase in health expenditures. Technological devices, which make pressure on health expenditures, affect the whole world while making technology management become a global concern and a long-term problem. This study aims to compare the efficiency of medical device use among 81 provinces in Turkey. The main objective of this study is to determine a national framework by reflecting the efficient and inefficient provinces in terms of medical device use and to make various recommendations accordingly.
Materials and Methods: The study is comprised of a two-stage analysis. Firstly, the Data Envelopment Analysis (DEA) and then the Ordinary Least Squares (OLS) were utilized respectively. The efficient and inefficient provinces regarding medical device use were identified through DEA while the factors affecting the efficiency of provinces through OLS.
Results: 22 provinces were found as efficient and 59 as inefficient among 81 provinces in total. According to the regression model, there is not any statistically significant effect of the variables such as metropolitan, rate of university graduates, gross domestic product per capita on the efficiency score (p>0.05); the number of physicians and old-age dependency rate has a statistically significant effect on the efficiency score (p≤0.05).
Conclusion: This study is considered to provide guiding information to health policy makers and planners through its results.

Kaynakça

  • Jonsson, E, Banta, D, Management of health technologies: an international view, BMJ: British Medical Journal, 1999, 319, 1293-1295.
  • World Health Organization, Health technology assessment of medical devices, Geneva: World Health Organization, 2011.
  • Green, A, Bennett, S, Sound choices: enhancing capacity for evidence-informed health policy, Geneva: Alliance for Health Policy and Systems Research & World Health Organization, 2007.
  • Altman, S.H, Blendon, R, Medical technology: the culprit behind health care costs? Proceedings of the 1977 Sun Valley Forum on National Health, 1977.
  • Banta, D, Health care technology as a policy issue. In: Banta D, Battista R, Gelband H, Jonsson E (eds) Health care technology and its assessment in eight countries. Washington, DC: United States Congress, 1995, 275-334.
  • Mohandas, A, Foley, K.A, Medical devices: adapting to the comparative effectiveness landscape, Biotechnology Healthcare, 2010, 7(2), 25-28.
  • Charnes, A, Cooper, W, Rhodes, E, Measuring the efficiency of decision making, European Journal of Operational Research, 1978, 2(6), 429–44.
  • Farrell, M.J, The measurement of productive efficiency, Journal of the Royal Statistical Society Series A (General) 1957, 120(3), 253-290.
  • Banker, R.D, Charnes, A, Cooper, W.W, Some models for estimating technical and scale ınefficiencies in data envelopment analysis, Management Science, 1984, 30(9), 1078-1092.
  • Charnes, A, Cooper, W, Rhodes, E, Evaluating program and managerial efficiency: An application of data envelopment analysis to program follow through, Management Science, 1981, 27(6), 668-697.
  • Chern, J.Y, Wan, T.T, The impact of the prospective payment system on the technical efficiency of hospitals, Journal of Medical Systems, 2000, 24, 159–172.
  • Sherman, H, Zhu, J, Service productivity management: Improving service performance using data envelopment analysis (DEA), Springer, USA, 2006.
  • Ozcan, Y.A, Health care benchmarking and performance evaluation, International Series in Operations Research & Management Science, Springer, USA, 2014.
  • Stöckl, D, Dewitte, K, Thienpont, L.M, Validity of linear regression in method comparison studies: Is it limited by the statistical model or the quality of the analytical input data? Clinical Chemistry, 1998, 44, 2340-2346.
  • Doi, K, Diagnostic imaging over the last 50 years: Research and development in medical imaging science and technology, Physics in Medicine & Biology, 2006, 51(13), R5-R27.
  • Quaday, K.A, Salzman, J.G, Gordon, B.D, Magnetic resonance imaging and computed tomography utilization trends in an academic ED, The American Journal of Emergency Medicine, 2014, 32(6), 524-528.
  • Semin, S, Demiral, Y, Dicle, O, Trends in diagnostic imaging utilization in a university hospital in Turkey, International Journal of Technology Assessment in Health Care, 2006, 22(4), 532-536.
  • Smith-Bindman, R, Miglioretti, D.L, Larson, E.B, Rising use of diagnostic medical imaging in a large integrated health system, Health Affairs, 2008, 27(6), 1491-1502.
  • Wang, L, Nie, J.X, Tracy, C.S, Moineddin, R, Upshur, R.E, Utilization patterns of diagnostic imaging across the late life course: a population-based study in Ontario, Canada, International Journal of Technology Assessment in Health Care, 2008, 24(4), 384-390.
  • Hillman, B.J, Goldsmith, J.C, The uncritical use of high-tech medical imaging, New England Journal of Medicine, 2010, 363(1), 4-6.
  • Hendee, W.R, Becker, G.J, Borgstede, J.P, Bosma, J, Casarella, W.J, Erickson, B.A, Maynard, C.D, Thrall, J.H, Wallner, P.E, Addressing overutilization in medical imaging, Radiology, 2010, 257(1), 240-245.
  • Lang, K, Huang, H, Lee, D.W, Federico, V, Menzin, J, National trends in advanced outpatient diagnostic imaging utilization: an analysis of the medical expenditure panel survey, 2000-2009, BMC Medical Imaging, 2013, 13(40), 1-10.
  • Weilburg, J.B, Sistrom, C.L, Rosenthal, D.I, Stout, M.B, Dreyer, K.J, Rockett, H.R, et al., Utilization management of high-cost imaging in an outpatient setting in a large stable patient and provider cohort over 7 years, Radiology, 2017, 284(3), 766-776.
  • Iglehart, J.K, Health insurers and medical-imaging policy—a work in progress, New England Journal of Medicine, 2009, 360, 1030-1037.
  • Baker, L, Birnbaum, H, Geppert, J, Mishol, D, Moyneur, E, The relationship between technology availability and health care spending: Attempts to address technology availability and rising costs could end up badly misguided if implications for quality are not considered, Health Affairs, 2003, 22(Suppl1), W3-537.
  • Maitino, A.J, Levin, D.C, Parker, L, Rao, V.M, Sunshine, J.H, Practice patterns of radiologists and nonradiologists in utilization of noninvasive diagnostic imaging among the Medicare population 1993–1999, Radiology, 2003, 228(3), 795-801.
  • Beinfeld, M.T, Gazelle, G.S, Diagnostic imaging costs: are they driving up the costs of hospital care? Radiology, 2005, 235(3), 934-939.
  • OECD. Computed tomography (CT) exams (indicator). doi: 10.1787/3c994537-en. Accessed 24 January 2019.
  • Okrah, K, Vaughan‐Sarrazin, M, Kaboli, P, Cram, P, Echocardiogram utilization among rural and urban veterans, The Journal of Rural Health, 2012, 28(2), 211-220.
  • Goode, A.P, Freburger, J.K, Carey, T.S, The influence of rural versus urban residence on utilization and receipt of care for chronic low back pain, The Journal of Rural Health, 2013, 29(2), 205-214.
  • Onega, T, Hubbard, R, Hill, D, Lee, C.I, Haas, J.S, Carlos, H.A, et al., Geographic access to breast imaging for US women, Journal of the American College of Radiology, 2014, 11(9), 874-882.
  • Cinaroglu, S, Baser, O, Spatial distribution of total number of medical devices in Turkey: A classification analysis, International Journal of Medicine and Public Health, 2017, 7(2), 102-106.
  • Sonğur, C, Top, M, Regional clustering of medical imaging Technologies, Computers in Human Behavior, 2016, 61, 333-343.
  • Ozcan, Y.A, Legg, J.S, Performance measurement for radiology providers: a national study, International Journal of Healthcare Technology and Management, 2014, 14(3), 209-221.
  • Keshtkaran, A, Barouni, M, Ravangard, R, Yandrani, M, Economic efficiency of radiology wards using data envelopment analysis: Case study of Iran, Health, 2014, 6(5), 311-316.
  • Hillman, B.J, Joseph, C.A, Mabry, M.R, Sunshine, J.H, Kennedy, S.D, Noether, M, Frequency and costs of diagnostic imaging in office practice—a comparison of self-referring and radiologist-referring physicians, New England Journal of Medicine, 1990, 323(23), 1604-1608.
  • Hillman, B.J, Olson, G.T, Griffith, P.E, Sunshine, J.H, Joseph, C.A, Kennedy, S.D, etal., Physicians' utilization and charges for outpatient diagnostic imaging in a Medicare population, Journal of the American Medical Association (JAMA), 1992, 268(15), 2050-2054.
  • Cherkin, D.C, Deyo, R.A, Wheeler, K, Ciol, M.A, Physician variation in diagnostic testing for low back pain: Who you see is what you get, Arthritis & Rheumatism: Official Journal of the American College of Rheumatology, 1994, 37(1), 15-22.
  • Rosen, M.P, Davis, R.B, Lesky, L.G, Utilization of outpatient diagnostic imaging: Does the physician’s gender play a role? Journal of General Internal Medicine, 1997, 12(7), 407-411.
  • Kanzaria, H.K, Hoffman, J.R, Probst, M.A, Caloyeras, J.P, Berry, S.H, Brook, R.H, Emergency physician perceptions of medically unnecessary advanced diagnostic imaging, Academic Emergency Medicine, 2015, 22(4), 390-398.
  • Sistrom, C, McKay, N.L, Weilburg, J.B, Atlas, S.J, Ferris, T.G, Determinants of diagnostic imaging utilization in primary care, The American Journal of Managed Care, 2012, 18(4), e135-e144.
  • Levin, D.C, Rao, V.M, Factors that will determine future utilization trends in diagnostic imaging, Journal of the American College of Radiology, 2016, 13(8), 904-908.
  • Toms, A.P, Cash, C.J, Linton, S.J, Dixon, A.K, Requests for body computed tomography: increasing workload, increasing indications and increasing age, European Radiology, 2001, 11(12), 2633-2637.
  • Latham, L.P, Ackroyd-Stolarz, S, Emergency department utilization by older adults: A descriptive study, Canadian Geriatrics Journal, 2014, 17(4), 118-125.
  • Lysdahl, K.B, Hofmann, B.M, What causes increasing and unnecessary use of radiological investigations? A survey of radiologists' perceptions, BMC Health Services Research, 2009, 9(1), 155-163.
  • Hu, M, A study on medical imaging equipment productivity and utilization. Proceedings of the 2011 Industrial Engineering Research Conference, 2011, 1-8.
Toplam 46 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Sağlık Kurumları Yönetimi
Bölüm Araştırma Makalesi
Yazarlar

Gülnur İlgün 0000-0003-0128-4001

Seda Sönmez 0000-0002-8773-6007

Murat Konca 0000-0002-6830-8090

Cuma Çakmak 0000-0002-4409-9669

Yayımlanma Tarihi 30 Aralık 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 9 Sayı: 4

Kaynak Göster

APA İlgün, G., Sönmez, S., Konca, M., Çakmak, C. (2022). Evaluation of the factors that affect the efficiency of diagnostic imaging technologies in Turkey: A two-stage data envelopment analysis. Celal Bayar Üniversitesi Sağlık Bilimleri Enstitüsü Dergisi, 9(4), 492-500. https://doi.org/10.34087/cbusbed.994765
AMA İlgün G, Sönmez S, Konca M, Çakmak C. Evaluation of the factors that affect the efficiency of diagnostic imaging technologies in Turkey: A two-stage data envelopment analysis. CBU-SBED. Aralık 2022;9(4):492-500. doi:10.34087/cbusbed.994765
Chicago İlgün, Gülnur, Seda Sönmez, Murat Konca, ve Cuma Çakmak. “Evaluation of the Factors That Affect the Efficiency of Diagnostic Imaging Technologies in Turkey: A Two-Stage Data Envelopment Analysis”. Celal Bayar Üniversitesi Sağlık Bilimleri Enstitüsü Dergisi 9, sy. 4 (Aralık 2022): 492-500. https://doi.org/10.34087/cbusbed.994765.
EndNote İlgün G, Sönmez S, Konca M, Çakmak C (01 Aralık 2022) Evaluation of the factors that affect the efficiency of diagnostic imaging technologies in Turkey: A two-stage data envelopment analysis. Celal Bayar Üniversitesi Sağlık Bilimleri Enstitüsü Dergisi 9 4 492–500.
IEEE G. İlgün, S. Sönmez, M. Konca, ve C. Çakmak, “Evaluation of the factors that affect the efficiency of diagnostic imaging technologies in Turkey: A two-stage data envelopment analysis”, CBU-SBED, c. 9, sy. 4, ss. 492–500, 2022, doi: 10.34087/cbusbed.994765.
ISNAD İlgün, Gülnur vd. “Evaluation of the Factors That Affect the Efficiency of Diagnostic Imaging Technologies in Turkey: A Two-Stage Data Envelopment Analysis”. Celal Bayar Üniversitesi Sağlık Bilimleri Enstitüsü Dergisi 9/4 (Aralık 2022), 492-500. https://doi.org/10.34087/cbusbed.994765.
JAMA İlgün G, Sönmez S, Konca M, Çakmak C. Evaluation of the factors that affect the efficiency of diagnostic imaging technologies in Turkey: A two-stage data envelopment analysis. CBU-SBED. 2022;9:492–500.
MLA İlgün, Gülnur vd. “Evaluation of the Factors That Affect the Efficiency of Diagnostic Imaging Technologies in Turkey: A Two-Stage Data Envelopment Analysis”. Celal Bayar Üniversitesi Sağlık Bilimleri Enstitüsü Dergisi, c. 9, sy. 4, 2022, ss. 492-00, doi:10.34087/cbusbed.994765.
Vancouver İlgün G, Sönmez S, Konca M, Çakmak C. Evaluation of the factors that affect the efficiency of diagnostic imaging technologies in Turkey: A two-stage data envelopment analysis. CBU-SBED. 2022;9(4):492-500.