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Diffusion of innovations in health care: Does the structural context determine its direction?

Published online by Cambridge University Press:  13 October 2010

Mattijs S. Lambooij
Affiliation:
National Institute for Public Health and the Environment (RIVM)
Peter Engelfriet
Affiliation:
National Institute for Public Health and the Environment (RIVM)
Gert P. Westert
Affiliation:
Tilburg University and National Institute for Public Health and the Environment (RIVM)

Abstract

Objectives: The aim of this study was to present and illustrate an instrument to measure the level of innovation at country level.

Methods: The data used are the Organisation for Economic Co-operation and Development (OECD) health data 2009, in particular the information on use of medical technology. Two composite scales expressing a relative level of adoption of innovations in health care are regressed, using multilevel regression analysis, on country characteristics. The country characteristics are selected as proxies on availability or scarcity of resources in a country. We expect that scarcity will promote adoption of innovations that enhance efficiency, and that availability of resources will promote advanced, expensive innovations.

Results: Two scales were constructed. One scale indicates the use of efficiency-enhancing innovations (day case treatment), and the other scale indicates availability of advanced technical innovations. The application of day case treatment is significantly associated with education level (+), the ratio of people aged 15–64 versus younger and older people (+) and the number of hospital beds (−). Availability of advanced medical devices are associated with the expenditure on health (+), demographic dependency (−), number of hospital beds (+), and the annual reduction of hospital beds (−).

Conclusions: Diffusion of innovations is influenced by characteristics of the country and of the healthcare system; fewer resources encourage diffusion of innovations that enhance efficiency and more resources encourage diffusion of complex, expensive devices. This indicates that decisions by healthcare professionals on which innovation to adopt is embedded in a context that is influenced and shaped by the availability of resources on macro level.

Type
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Copyright
Copyright © Cambridge University Press 2010

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References

REFERENCES

1. Bech, M, Christiansen, T, Dunham, K, et al. The influence of economic incentives and regulatory factors on the adoption of treatment technologies: A case study of technologies used to treat heart attacks. Health Econ. 2009;18:11141132.Google Scholar
2. Cronbach LJ. Coefficient alpha and the internal structure of tests. Psychometrika. 1951;16:297334.CrossRefGoogle Scholar
3. Cutler, DM, McClellan, M. Is technological change in medicine worth it? Health Aff (Millwood). 2001;20:1129.Google Scholar
4. de Hollander, AEM, Hoeymans, N, Melse, JM, van Oers, JAM, Polder, JJ. Care for health. Bilthoven: National Institute for Public Health and the Environment; 2007.Google Scholar
5. Gratwohl, A, Passweg, J, Baldomero, H, Horisberger, B, Urbano-Ispizua, A. Economics, health care systems and utilization of haematopoietic stem cell transplants in Europe. Br J Haematol. 2002;117:451468.CrossRefGoogle ScholarPubMed
6. Lazaro, P, Fitch, K. The distribution of big ticket medical technologies in OECD countries. Int J Technol Assess Health Care. 1995;11:552570.CrossRefGoogle ScholarPubMed
7. Newhouse, JP. Medical care costs: How much welfare loss? J Econ Perspect. 1992;6:323.Google Scholar
8. OECD. OECD health data 2009. Paris: OECD; 2009.Google Scholar
9. Oh, EH, Imanaka, Y, Evans, E. Determinants of the diffusion of computed tomography and magnetic resonance imaging. Int J Technol Assess Health Care. 2005;21:7380.CrossRefGoogle ScholarPubMed
10. Raudenbush, SW, Bryk, AS. Hierarchical linear models: Applications and data analysis methods. Thousand Oaks, CA: Sage; 2002.Google Scholar
11. Rogers, EM. Diffusion of innovations. 4th ed. New York: The Free Press; 1995, [1962].Google ScholarPubMed
12. Snijders, T, Bosker, R. Multilevel analysis: An introduction to basic and advanced multilevel modeling. London: SAGE publications; 1999.Google Scholar
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