Open Access Research article

Nation-scale adoption of new medicines by doctors: an application of the Bass diffusion model

Adam G Dunn1*, Jeffrey Braithwaite2, Blanca Gallego1, Richard O Day3, William Runciman24 and Enrico Coiera1

Author Affiliations

1 Centre for Health Informatics, Australian Institute of Health Innovation, University of New South Wales, Sydney, 2052, NSW, Australia

2 Centre for Clinical Governance Research in Health, Australian Institute of Health Innovation, University of New South Wales, Sydney, Australia

3 Department of Clinical Pharmacology, St Vincent’s Hospital, University of New South Wales, Sydney, Australia

4 School of Psychology, Social Work and Social Policy, University of South, Sydney, Australia

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BMC Health Services Research 2012, 12:248  doi:10.1186/1472-6963-12-248

Published: 10 August 2012

Abstract

Background

The adoption of new medicines is influenced by a complex set of social processes that have been widely examined in terms of individual prescribers’ information-seeking and decision-making behaviour. However, quantitative, population-wide analyses of how long it takes for new healthcare practices to become part of mainstream practice are rare.

Methods

We applied a Bass diffusion model to monthly prescription volumes of 103 often-prescribed drugs in Australia (monthly time series data totalling 803 million prescriptions between 1992 and 2010), to determine the distribution of adoption rates. Our aim was to test the utility of applying the Bass diffusion model to national-scale prescribing volumes.

Results

The Bass diffusion model was fitted to the adoption of a broad cross-section of drugs using national monthly prescription volumes from Australia (median R2 = 0.97, interquartile range 0.95 to 0.99). The median time to adoption was 8.2 years (IQR 4.9 to 12.1). The model distinguished two classes of prescribing patterns – those where adoption appeared to be driven mostly by external forces (19 drugs) and those driven mostly by social contagion (84 drugs). Those driven more prominently by internal forces were found to have shorter adoption times (p = 0.02 in a non-parametric analysis of variance by ranks).

Conclusion

The Bass diffusion model may be used to retrospectively represent the patterns of adoption exhibited in prescription volumes in Australia, and distinguishes between adoption driven primarily by external forces such as regulation, or internal forces such as social contagion. The eight-year delay between the introduction of a new medicine and the adoption of the prescribing practice suggests the presence of system inertia in Australian prescribing practices.

Keywords:
Adoption; Diffusion of innovation; Decision-making; Prescribing behaviour; Australia; Evidence-based practice