Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-30T20:22:00.267Z Has data issue: false hasContentIssue false
  • English
  • Français

Determinants of the diffusion of computed tomography and magnetic resonance imaging

Published online by Cambridge University Press:  02 March 2005

Eun-Hwan Oh ,
Yuichi Imanaka  and
Edward Evans
Eun-Hwan Oh
Affiliation:
Kyoto University
Yuichi Imanaka
Affiliation:
Kyoto University
Edward Evans
Affiliation:
Kyoto University
Get access Rights & Permissions [Opens in a new window]

Abstract

Objectives: The aim of this study is to explain factors influential to the diffusion of computed tomography (CTs) and magnetic resonance imaging (MRIs).

Methods: Variables were identified from a review of the literature on the diffusion of health technologies. A formal process was applied to build a conceptual model of the mechanism that drives technology diffusion. Variables for the analysis were classified as predisposing, enabling, or reinforcing factors, in keeping with a model commonly used to explain the diffusion of health behaviors. Multiple regression analysis was conducted using year 2000 OECD data.

Results: The results of this study showed that total health expenditure per capita (p < .01, both CTs and MRIs) and flexible payment methods to hospitals (p < .05, both CTs and MRIs) were significantly associated with the diffusion of CTs and MRIs (adjusted R2 = 0.477, 0.656, respectively).

Conclusions: This study presents a systematically developed model of the mechanism governing technology diffusion. Important findings from the study show that purchasing power, represented by total health expenditure per capita and economic incentives to hospitals in the form of flexible payment methods, were positively correlated with diffusion. Another important achievement of our model is that it accounts for all thirty OECD member countries without excluding any as outliers. This study shows that variation across countries in the diffusion of medical technology can be explained well by a logical model with multiple variables, the results of which hold profound implications for health policy regarding the adoption of innovations.

Keywords

Technology diffusionCTMRIPayment method
Type
GENERAL ESSAYS
Information
International Journal of Technology Assessment in Health Care , Volume 21 , Issue 1 , January 2005 , pp. 73 - 80
Copyright
© 2005 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Atella V. 2003. The relationship between health policies, medical technology trend, and outcomes: A perspective from the TECH Global Research Network. In: A disease-based comparison of health systems: What is best and at what cost? Paris: OECD;
Banta HD. Embracing or rejecting innovations: Clinical diffusion of health care technology. In: Reiser SJ, Anbar M, eds. 1984. The machine at the bedside: Strategies for using technology in patient care. New York: Cambridge University Press;
Banta HD, Behney CJ, Willems JS. 1981. Toward rational technology in medicine: Considerations for health policy. New York: Springer;
Battista RN. 1989 Innovation and diffusion of health-related technologies. Int J Technol Assess Health Care. 5: 227– 248.Google Scholar
Cromwell J, Kanak J. 1982 The effects of prospective reimbursement programs on hospital adoption and service sharing. Health Care Financ Rev. 4: 67.Google Scholar
Donaldson C, Gerard K. 1993. Economics of health care financing: The visible hand. London: Palgrave Macmillan;
Drummond M. 1994 Evaluation of health technology: Economic issues for health policy and policy issues for economic appraisal. Soc Sci Med. 38: 1593– 1600.Google Scholar
Fagnani F, Moatti JP, Weill C. 1987 The diffusion and use of diagnostic imaging equipment in France: The limits of regulation. Int J Technol Assess Health Care. 3: 531– 543.Google Scholar
Field A. 2000. Discovering statistics using SPSS for Windows. London: SAGE Publications;
Green LW, Eriksen MP, Schor EL. 1988 Preventive practices by physicians: Behavioral determinants and potential interventions. Am J Prev Med. 4 (Suppl 4): 101– 117.Google Scholar
Green LW, Kreuter MW. 1991. Health promotion planning: An educational and environmental approach. Mountain View: Mayfield Publishing Company;
Hillman AL, Schwartz JS. 1985 The adoption and diffusion of CT and MRI in the United States: A comparative analysis. Med Care. 23: 1283– 1294.Google Scholar
Hillman BJ, Neu CR, Winkler JD, et al. 1987 The diffusion of magnetic resonance imaging scanners in a changing US health care environment. Int J Technol Assess Health Care. 3: 545– 559.Google Scholar
Lázaro P, Fitch K. 1995 The distribution of “big ticket” medical technologies in OECD countries. Int J Technol Assess Health Care. 11: 552– 570.Google Scholar
Lee RH, Waldman DM. 1985 The diffusion of innovations in hospitals: Some econometric considerations. J Health Econ. 4: 373– 380.Google Scholar
McGivney WT. 1988 Regulatory, coverage and reimbursement changes: Implications for diffusion of technology in radiology. Invest Radiol. 23: 795– 798.Google Scholar
McGuire A, Henderson J, Mooney G. 1988. The economics of health care: An introductory text. London: Routledge and Kegan Paul;
Nelson GD. 1994; Preserving the milieu for medical innovation. Health Aff (Millwood). 13: 112– 114.Google Scholar
Newhouse JP. 1993; An iconoclastic view of health cost containment. Health Aff (Millwood). 12 (Suppl): 152– 171.Google Scholar
OECD. 2003. OECD health data 2003. 4th ed. Paris: OECD;
Romeo AA, Wagner JL, Lee RH. 1984; Prospective reimbursement and the diffusion of new technologies in hospitals. J Health Econ. 3: 1– 24.Google Scholar
Russell L, Sisk J. 1988; Medical technology in the United States: The last decade. Int J Technol Assess Health Care. 4: 269– 286.Google Scholar
Slade EP, Anderson GF. 2001; The relationship between per capita income and diffusion of medical technologies. Health Policy. 58: 1– 14.Google Scholar
Sloan FA, Valvona J, Perrin JM, Adamache KW. 1986; Diffusion of surgical technology: An exploratory study. J Health Econ. 5: 31– 61.Google Scholar
Tanon C, Rogers E. 1975. Diffusion research methodology: Focus on health care organizations. In: Gordon G, Fisher L, eds. The diffusion of medical technology. Cambridge: Ballinger;
Weisbrod BA. 1994. The nature of technological change: Incentives matter! In: Gelijns AC, Dawkins HV, eds. Adopting new medical technology. Washington DC: National Academy Press;
White K. 1974; Contemporary epidemiology. Int J Epidemiol. 3: 295– 303.Google Scholar