Over the past 25 years, rising medical cost has been a common concern of policy-makers as well as researchers. In the 1970s, methodology intended to assess both the cost and benefits gained from health-care expenditures entered the medical literature. For simplicity, we refer to those cost-effectiveness analyses using quality-adjusted life years (QALYs) as an outcome measure as cost-utility analyses (CUAs).

Cost-utility analysis is a methodological approach to assessing the value of a given health technology, program, or intervention. As such, it can be considered a process innovation, one of many designed to improve the ability to make informed decisions about utilization and coverage of medical interventions. One concern about CUAs is that they have not been broadly used by decision-makers in health care. This finding may be because CUAs have been performed by researchers and may not be broadly accessible to or interpretable by policy-makers (4;5;11). Medical practitioners and policy-makers may be unsure about the correctness of the analyses or its real-life implications for the medical-care system (7;8). In addition, the analyses may not be useful because the end goals are constrained by pressures other than simple economic efficiency.

The use of an innovation also depends on the stage of innovation. Typologies for the stage of innovation have been described previously. Generally, innovation begins with realization of the need for innovation. A second stage enacts ideas for the specific form of the innovation. In the third stage, the innovation is first brought into practical use. A final stage includes the routine use of the innovation (9). The recommendation of the Panel on Cost-Effectiveness Analysis in Health and Medicine led to the enactment of a plan for a specific form of cost-utility analysis (referred to as a “reference case”) so that analyses would be comparable across different medical practices and disease types (3). Since that time, some organizations have begun to use CUA more broadly in decision making (2;10). In addition to implementation, innovation faces the question of diffusion or dissemination. Potential users of an innovation need to become aware of the innovation and then make a decision about whether or not to adopt it. Individuals and organizations can be considered along an adoption continuum starting with the innovators and progressing from early adopters to those who wait until the technology is well-developed and understood (1;9). Successful implementation in one setting does not necessarily ensure broad dissemination of an innovation. Our specific interest in this article was to examine the diffusion of CUAs through the medical literature to determine what patterns can be seen and how they correspond with general expectations about the diffusion of process innovations.

RESULTS

Over the study period, a total of 539 studies were identified and included. The number of cost-utility analyses published on an annual basis rose steadily between 1976 and 1999 and then leveled out at approximately 85 publications per year. When the average number of publications is plotted against 3-year periods (Figure 1), the plot reveals an S-shaped curve that parallels curves seen throughout the literature on the diffusion of innovations (6).

Dissemination curve for cost-utility analyses (CUA) publications.

The 539 studies appeared in a total of 204 different journals and covered many clinical areas, particularly cardiology, oncology, and pharmacology. The journals themselves included general medical, health services research, and clinical specialties, including pharmacology and nursing. Figure 2 shows the percentage of articles appearing in each journal type over time. It appears that, whereas CUAs were initially published more frequently in general medical or health services research journals, there was a clear increase in the diffusion of CUA into subspecialty journals over time. Table 1 shows dates of first publication of identified CUAs in specific specialties as well as the pattern of publication in that specialty area over time. The concentration ratio for research centers as measured by the HHI decreased over time (Figure 3).

Changes in percentage of articles published in each journal type over time. HSR, health services research; GENMED, general medicine; MEDSPEC, clinical specialty.

Change in Herfindhal–Hirschman Index (HHI) over time.