Although technological change processes in agriculture and industry have been extensively studied, studies of technological change processes in the service sector, are, to date, limited in number. Findings from this study of innovative health care technologies and their diffusion in the Australian service sector can usefully be compared with other studies of innovation and diffusion in both orthodox and unorthodox settings, in this country and overseas.
Although the predominance of derivative technology in Australia is further underscored by this study, innovation lags are noticeably shorter than has commonly been found to be the case. As has previously been found to be the case in the industrial sector, diffusion in the medical care sector can be seen to be, overall, a slow process.
Conventional diffusion theory postulates that the diffusion process typically follows an S-shaped growth curve, and can be modelled using a cumulative normal distribution or a logistic function. Supporting examples can be drawn from studies of diffusion in the agricultural, industrial and health care sector. Dissemination patterns of medical technologies in Australia, for which satisfactory data were available and for which time functions were fitted, have likewise been found to follow the typical pattern and can be adequately described by logistic models.
However, those routinely used pictorial representations of diffusion fail to capture some important aspects of what is a complex process. Because it is commonplace for technological innovations to be made acceptable to a widened range of users, through modification or improvement, or because novel uses are found as experience with a technology accumulates, the “technological ceiling” implicit in the idea of spread within a defined potential population has only a limited usefulness. Because innovations inevitably change over time, the S-curve is at best only an approximation to the actual historical processes it seeks to describe.
Technological leaders’ and followers in the medical innovation diffusion process have been identified and similarities can be drawn with findings in the industrial sector. In the medical care sector, as in the industrial sector, large decision units tend to be the first to adopt and innovations then spread downwards to smaller units over time. The rate of diffusion is typically fastest among large units.
Because decision-makers typically experience great difficulties in obtaining relevant, useful information about an innovation's likely benefits, technological advantage may mistakenly be taken to imply medical advantage, just as, elsewhere, it has been found to imply economic advantage. Although medical decision-makers always perceive good reasons for adopting a new technology, medical advantage, that dimension of relative advantage which assumes major significance in health care technology diffusion, may fall far short of initial expectations.
Unorthodox aspects of medical innovation diffusion have their origins in the unusual environment in which medical care is delivered. In the industrial sector, the size of the investment involved in adopting an innovation is a stimulus to decision-makers to undertake estimates of profits or cost savings likely to be derived from its use. In contrast, in the medical care sector, incentives for adopters to undertake pre-decision estimates of a technology's worth are reduced, or removed entirely, because costs are largely covered by third parties. Inequalities of information between the physician and the patient impose a social obligation to provide the best treatment currently available.
Several policy implications can be drawn from this study. The data deficiencies of the Australian health care system j as evidenced by the problems which have beset efforts to determine diffusion patterns, emphasise the need for formal national registers of medical technologies. A review of the history of the innovations studied, their predecessors and successors, emphasises that technologies, initially thought to be of worth, are too often introduced, only to be later discarded. Comprehensive technology assessment programmes can provide viable alternatives to the ad-hoc approach which currently dominates medical decision-making. Future resource allocation decisions in medicine must be based upon awareness of the finiteness of resources able to be devoted to health care and of the necessity for making informed choices among alternatives.