Volume 8, Issue 4 (Autumn 2020)                   Iran J Health Sci 2020, 8(4): 20-27 | Back to browse issues page


XML Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Akbari M, Assari Arani A, Akbari M E, Sahabi B, Olyaeemanesh A. Supplier-Induced Demand in Diagnostic MRI of Primary Breast Cancer. Iran J Health Sci 2020; 8 (4) :20-27
URL: http://jhs.mazums.ac.ir/article-1-735-en.html
Tarbiat Modares University, Tehran, Iran , assari_a@modares.ac.ir
Abstract:   (2176 Views)
Background: Supplier-induced demand (SID) refers to the concept that healthcare providers may deliver services to patients that are not medically necessary. An estimation of the extent to which this event has occurred can be insightful for policymaking and guiding health systems. This study aimed to investigate the extent to which SID presents itself during diagnostic MRI (magnetic resonance imaging) for primary breast cancer. Methods: In this cross-sectional study, data were obtained using questionnaires from a random sample of 310 cases. To identify patients who were candidates for undergoing a necessary diagnostic MRI, we employed the international clinical guidelines with the confirmation of our expert panelists. With their assistance, a comprehensive index was created to screen those who were affected by SID. Results: Of the respondents, 94.1% had undergone an unnecessary diagnostic MRI and, thus, were likely affected by SID, which indicated the lack of sovereignty of clinical guidelines in the prescription of MRI diagnosis imaging. Conclusion: This study supported the SID hypothesis and the unnecessary demand for diagnostic MRI in primary breast cancer. In addition, our evidence indicated that excessive costs were imposed; these can positively influence policymakers’ decisions regarding healthcare management. 
Full-Text [PDF 433 kb]   (1040 Downloads)    
Type of Study: Original Article | Subject: Hospital Management

References
1. Bickerdyke I, Dolamore R, Monday I, Preston R. Supplier-Induced Demand for Medical Services. Canberra: Productivity Commission Staff Working Paper; 2002. p. 1-113.
2. McGuire TG. Physician Agency. HANDBOOK OF HEALTHE ECONOMICS.1: Elsevier;2000.p. 461-536. [DOI:10.1016/S1574-0064(00)80168-7]
3. Getzen TE. Health Economics: fundamentals and flow of funds: John Wiley & Sons; 2004.
4. Reinhardt UE. Economists in health care: saviors, or elephants in a porcelain shop? American Economic Review. 1989;79(2):337-42.
5. Latest global cancer data: Cancer burden rises to 18.1 million new cases and 9.6 million cancer deaths in 2018: International Agency for Research on Cancer; 2018 [Available from: https://www.iarc.fr/wp-content/uploads/2018/09/pr263_E.pdf.
6. Akbari A, Khayamzadeh M, Salmanian R, Ghanbari Motlagh A, Roshandel G, Nouri M, et al. National Cancer Mortality-to-Incidence Ratio (MIR) in Iran (2005 - 2014). International Journal of Cancer Management. 2019;12(6). [DOI:10.5812/ijcm.94145]
7. Grilli R, Chiesa V. Overuse in cancer care: do European studies provide information useful to support policies? Health Research Policy and Systems. 2018;16(1):12. [DOI:10.1186/s12961-018-0287-z] [PMID] [PMCID]
8. Morrison A. Appropriate utilization of advanced diagnostic imaging procedures: CT, MRI, and PET/CT. Environmental Scan. 2013;39.
9. Grol R, Grimshaw J. From best evidence to best practice: effective implementation of change in patients' care. Lancet. 2003;362(9391):1225-30. [DOI:10.1016/S0140-6736(03)14546-1]
10. Zhang BN, Cao XC, Chen JY, Chen J, Fu L, Hu XC, et al. Guidelines on the diagnosis and treatment of breast cancer (2011 edition). Gland Surgery 2012;1(1):39-61.
11. Senkus E, Kyriakides S, Ohno S, Penault-Llorca F, Poortmans P, Rutgers E, et al. Primary breast cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Annals of Oncology. 2015;26 Suppl 5:v8-30. [DOI:10.1093/annonc/mdv298] [PMID]
12. Appavoo S, Aldis A, Causer P, Crystal P, Mesurolle B, Mundt Y. CAR practice guidelines and technicals standards for breast imaging and intervention. Canadian Association of Radiologists Journal. 2012-Modified On 2016.
13. Parker S. Clinical guidelines for the management of breast cancer. England: NHS; 2019.
14. Network NCC. Breast Cancer (Version 2.2019). 2019.
15. Australia C. The investigation of a new breast symptom: a guide for General Practitioners 2017, Summary of the development process and methodology. Cancer Australia; 2017.
16. Mooney G. Key issues in health economics: Harvester Wheatsheaf New York; 1994.
17. Folland S, Goodman AC, Stano M. The Economics of Health and Health Care: Taylor & Francis; 2017. [DOI:10.4324/9781315101781]
18. Fuchs VR. The supply of surgeons and the demand for operations. Journal of Human Resources. 1978;13 Suppl:35-56. [DOI:10.2307/145247] [PMID]
19. Labelle R, Stoddart G, Rice T. A re-examination of the meaning and importance of supplier-induced demand. Journal of Health Economics. 1994;13(3):347-68. [DOI:10.1016/0167-6296(94)90036-1]
20. Mohammadshahi M, Yazdani S, Olyaeemanesh A, Akbari Sari A, Yaseri M, Emamgholipour Sefiddashti S. A Scoping Review of Components of Physician-induced Demand for Designing a Conceptual Framework. Journal of Preventive Medicine and Public Health. 2019;52(2):72-81. [DOI:10.3961/jpmph.18.238] [PMID] [PMCID]
21. Grol R. Successes and failures in the implementation of evidence-based guidelines for clinical practice. Medical Care. 2001;39(8 Suppl 2):Ii46-54. [DOI:10.1097/00005650-200108002-00003] [PMID]
22. Yurttakal AH, Erbay H, İkizceli T, Karaçavuş S. Detection of breast cancer via deep convolution neural networks using MRI images. Multimedia Tools and Applications. 2020;79(21):15555-73. [DOI:10.1007/s11042-019-7479-6]
23. Feng H, Cao J, Wang H, Xie Y, Yang D, Feng J, et al. A knowledge-driven feature learning and integration method for breast cancer diagnosis on multi-sequence MRI. Magnetic Resonance Imaging. 2020. [DOI:10.1016/j.mri.2020.03.001] [PMID]
24. Sheth D, Giger ML. Artificial intelligence in the interpretation of breast cancer on MRI. Journal of Magnetic Resonance Imaging. 2020;51(5):1310-24. [DOI:10.1002/jmri.26878] [PMID]
25. Azzam H, Kamal R, El-Assaly H, Metwally LIA. The value of dynamic contrast-enhanced MRI in differentiating triple-negative breast cancer from other subtypes. Egyptian Journal of Radiology and Nuclear Medicine. 2019;50(1):106. [DOI:10.1186/s43055-019-0118-4]
26. Saslow D, Boetes C, Burke W, Harms S, Leach MO, Lehman CD, et al. American Cancer Society guidelines for breast screening with MRI as an adjunct to mammography. A Cancer Journal for Clinicians 2007;57(2):75-89. [DOI:10.3322/canjclin.57.2.75] [PMID]
27. Nakashima K, Uematsu T, Harada TL, Takahashi K, Nishimura S, Tadokoro Y, et al. MRI-detected breast lesions: clinical implications and evaluation based on MRI/ultrasonography fusion technology. Japanese Journal of Radiology. 2019;37(10):685-93. [DOI:10.1007/s11604-019-00866-8] [PMID]
28. Bakker MF, de Lange SV, Pijnappel RM, Mann RM, Peeters PHM, Monninkhof EM, et al. Supplemental MRI Screening for Women with Extremely Dense Breast Tissue. New England Journal of Medicine. 2019;381(22):2091-102. [DOI:10.1056/NEJMoa1903986] [PMID]
29. Haas CB, Nekhlyudov L, Lee JM, Javid SH, Bush M, Johnson D, et al. Surveillance for second breast cancer events in women with a personal history of breast cancer using breast MRI: a systematic review and meta-analysis. Breast Cancer Research and Treatment. 2020;181(2):255-68. [DOI:10.1007/s10549-020-05637-y] [PMID] [PMCID]

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

 

Designed & Developed by : Yektaweb