Conceptual Model

From our review of fifty-one medical technology diffusion studies, we identified a broad range of factors. We then grouped these factors into categories, using an approach similar to that described by Banta (2). The five categories we defined were as follows: (i) Purchasing Power; (ii) Patient Needs; (iii) Physician Demand; (iv) Government Regulation; and (v) Payment Methods. These five categories became the dimensions of our conceptual model and from each we selected one factor for later inclusion in a statistical model.

To illustrate the role that each of these dimensions plays in our model, we have further classified them as being predisposing factors, enabling factors, or reinforcing factors. Predisposing factors are those that provide the basic motivation for diffusion; enabling factors are factors that enable a motivation to be realized; and reinforcing factors are factors that sustain a level of diffusion once it has been reached. In our conceptual model, Patient Needs and Physician Demand are what we would consider predisposing factors—they set the stage and provide the actors' motivations. Purchasing Power is the enabling factor in our model and determines to what extent needs and demands can ultimately be met. Finally, Payment Methods and Government Regulation are reinforcing factors that provide the incentives necessary to sustain diffusion. Figure 1 presents a graphic representation of our diffusion model mapped in this way. Readers familiar with health promotion planning may recognize that we have borrowed this metaphor from the PRECEDE framework (11). It is not surprising that common mechanisms of behavior diffusion can be found between the diffusion of behavior to adopt health promotion practices and the diffusion of behavior to adopt health care technology.

A proposed model of determinants of technology diffusion.

Green et al. (10) explain behavioral determinants of preventive practices by physicians using a model with similar structure to ours, in which physician's values, beliefs, attitudes, and perspectives are described as predisposing factors. The authors include necessary skills and resources, such as space or materials, as enabling factors; and physician rewards as reinforcing factors.

It is widely assumed that factors belonging to the category Purchasing Power are positively related with the diffusion. Simply put, countries that can afford it will tend to adopt expensive new technologies faster (14;23). Gross domestic product (GDP) per capita is an example of the kinds of factors found in this category and can be interpreted as a crude measure of purchasing power. Another more health-care specific measure, total health expenditure (THE) as a fraction of GDP, is also included in this category.

Lázaro and Fitch (14) analyzed the relationships between technology diffusion and GDP per capita and health-care expenditure (HCE) per capita, both separately and together. However, HCE per capita is a function of GDP per capita and the fraction of GDP spent on health care, so HCE per capita (or THE per capita) alone should be enough to represent the influence purchasing power exerts on health care. We, therefore, selected THE per capita to represent purchasing power in our statistical model, with the expectation that it would allow us to measure the effect of the real money expenditure on health.

The category of Patient Needs includes measures that directly or indirectly express the level of demand generated by patients. Technology is more rapidly diffused where there are populations with a high prevalence of disease requiring treatment with advanced medical technologies (18;27). To the extent that this is true, technology diffusion then is disease-specific. A comparison of countries by disease structure would be very complicated; however, the percentage of a population over 65 years of age may be a reasonable proxy. As a population ages, demand for sophisticated medical technologies is likely to increase. We, therefore, selected this simple measure of population age structure to represent Patient Needs in our statistical model.

Physician Demand represents demand generated by technology adopters. Aspects, for example types of ownership, and the level of competition between health-care providers can be included in this category. The number of physicians per capita is a good indication of Physician Demand, because competition is proportional to the number of physicians. If the population remains fixed and the number of physicians increases, each physician on average will see fewer patients. As the number of patients per physician decreases, physicians feel increasing pressure to order more tests, prescribe more drugs, and perform more procedures on a per patient basis to maintain their income. Thus, as the number of physicians per capita increases, supplier-induced demand increases, positively stimulating technology diffusion (24). Based on this assumption, we selected the number of physicians per capita to represent Physician Demand in later statistical analysis.

The category of Government Regulation includes metrics that represent the level and nature of regulation. Most of these metrics will be negatively related with the extent of technology diffusion, given that regulation typically works against market orientation in the health sector (8;13;16;22). Strict regulation to limit the diffusion and utilization of medical technologies is a comparably easy strategy to contain health care costs. International comparisons in this category, however, are complicated by the fact that there is no simple quantitative measure of government controls. Public expenditure on health, however, is an indirect way to gage government involvement. When a large share of total health expenditure is covered by public funds, the government will have more leverage to restrict spending. Conversely, when private funds cover the majority of spending on health, the government will tend to have less control over the diffusion of technology. By this logic, the ratio of public spending on health to total health expenditure will vary inversely to technology diffusion. This ratio, therefore, was selected to represent Government Regulation in our regression model.

The last category in our model is Payment Methods. The method of payment can alternatively encourage or discourage the diffusion of new technologies. For example, fixed or inflexible payment methods, like global budgets, provide neutral or negative incentives for the adoption of new technologies, whereas flexible fee-for-service reimbursement mechanisms positively encourage adoption.

Many studies have acknowledged the important influence payment methods have on technology diffusion (4;5;7;12;15;18;21;26) , but none have addressed payment to hospitals and physicians separately. And yet, especially where capital-intensive technologies such as CTs and MRIs are concerned, hospitals and physicians play very different roles in the adoption and use of medical technology. For this reason, we elected to treat them separately.

Hospital payment methods can be grouped several different ways. Donaldson and Gerard characterized payment methods as being either retrospective or prospective, and indicated that a third category could be added if competition (e.g., capitation and per-capita payments to HMO's) were included (6). Another commonly used scheme includes global budgeting, per diem payments, and diagnosis-related group reimbursement (17). Yet another combination includes fixed global budgets, additional payments for the provision of more costly treatments, and fee-for-service payments (1).

Physician payment methods are more easily differentiated. Fee-for-service payments and salary payments represent two ends of a spectrum. Capitation payments, typically used in the context of primary care, fit between these two polls. In this study, capitation payments are ignored, because we are only concerned with hospital-based specialists. Instead, a hybrid of fee-for-service and salary payment is considered as a third option.

To simplify nomenclature, we elected to classify hospital and physician payment systems into three categories—fixed, moderate, and flexible—based on regulatory intensity (Table 1). “Fixed” payment methods leave little room for providers to influence revenue. Global budget methods to hospitals or salary payment methods in general terms are included in this category. “Flexible” methods are those that allow providers more autonomy. This category is composed of methods that calculate reimbursements on a per case or per diem basis. Payment systems that fall between these two poles, prospective payment methods for example, are deemed “moderate.”

Before moving on, it is important to note that we have made a distinction between payment methods to physicians and demand generated by physicians. The Payment Methods category just described includes supply factors that are directly derived from payment systems, whereas the Physician Demand category described earlier contains factors related to characteristics of physicians or their environment, for example, competition. Although it may seem subtle, this distinction is in keeping with supply-induced theory.

In addition to these five categories, it could be argued that the nature of the technology itself should play a part in determining the extension of its own diffusion. For example, the marginal clinical advantage CTs and MRIs hold over X-rays should encourage their adoption (2;12;25). For the purpose of international comparison, however, clinical advantages and disadvantages need not be considered, because they do not vary from country to country.

Statistical Model

After a correlation analysis to discover covariation between individual factors, we conducted a multiple regression analysis to identify key correlates with the diffusion of CTs and MRIs. Our multiple regression analysis took the following form:

where ln N_i is the natural logarithm of the number of CTs and MRIs per million population for country i.

The explanatory variables in our statistical model are the natural logarithm of total health expenditure per capita in US$ at purchasing power parity (ln THEPC_i), the share of public expenditure in THE (PETHE_i), the percentage of population 65 years of age or older (AGED_i), the natural logarithm of the number of physicians per 1,000 population (ln PHYS_i), moderate payment methods to hospitals (HOSMOD_i), flexible payment methods to hospitals (HOSFXB_i), moderate payment methods to physicians (PHYMOD_i), and flexible payment methods to physicians (PHYFXB_i). The constant β₀ is the intercept, β₁ through β₈ are coefficients of independent variables, and ε_i is an error term. Note that independent variables representing fixed payment methods are not included in the equation, because they were designated as comparison groups for both hospitals and physicians.

Data

Year 2000 data from all thirty OECD member countries was used in the analysis. OECD data was selected both because it represents the wide variation that exists among rich countries in the diffusion of CTs and MRIs and because it is readily accessible and of sufficient quality to allow meaningful international comparison. In cases where year 2000 data were not available, adjusted data from the most current available year was substituted. General information regarding factors selected to explain the diffusion of CTs and MRIs is shown in Table 2.

Information on payment systems was gathered from OECD Health Data 2003 (20) and other sources. (A complete list of references is available on request from the authors.) Because most payment systems are complex, it was frequently difficult to assign a country to a single payment category. For the purpose of our analysis though, a subjective, “best-fit” assignment was made. Hybrid payment systems that were too complex to categorize as either strict or flexible were designated as moderate.