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PubMed2001

The relationship between per capita income and diffusion of medical technologies.

It is commonly known that per capita income is correlated with the level of health care spending and that technology is a major factor in explaining the increase in health care spending. This study examines differences in the rate of diffusion of medical technologies in Organization for Economic Cooperation and Development countries between 1975 and 1995. We find that the importance of income in explaining the long-term availability of a technology generally declines over time and becomes insignificant for some technologies. In other words, more affluent countries are earlier adopters of new technologies, but access to technology becomes less dependent on income over time. The evidence also suggests that the effects of reimbursement incentives are greater for purchases of diagnostic technologies than for lifesaving technologies and that reimbursement incentive effects are less significant for older technologies.

Biomedical Technology↗
PubMed1955

Laboratory technicians; the Clinical Laboratory Law and its meaning to private physicians.

The present laws and regulations relating to clinical laboratories in California are the outcome of over a quarter century of cooperative development. The medical profession, public health department, laboratory workers, and the legislature have worked together in this development.At first the system of certifying technicians and laboratories was on a voluntary basis. The clinical laboratory law in effect legalized and made generally applicable a system which had already been accepted voluntarily. The application of the clinical laboratory law provides physicians a reasonable assurance that competence and reliability will prevail in clinical laboratory operation. Of great importance is the conduct of proper training programs by approved laboratories. Since modern medical practice is so dependent on accurate clinical laboratory work it is essential that special effort be directed by physicians toward influencing young people to enter the profession of medical technology.

Biomedical Technology↗
PubMedDate not supplied

Critical care nurses' experiences when technology malfunctions.

When caring for critically ill patients, critical care nurses work with technology every day. Technology and equipment malfunctions can have a profound effect on nurses' practice and self-image. In this article, a descriptive phenomenological methodology was chosen to explicate the experience of seven critical care nurses. While participants realized that machines might malfunction, they experienced surprise, shock, and feelings of being "let down" and inadequate when malfunctions occurred. They questioned their competence and felt malfunctioning technology jeopardized their credibility and professional image. These findings are useful when structuring educational sessions on technology and in facilitating a supportive environment for critical care nurses when technology malfunctions.

Adult↗
PubMed2002

Assessing the learning curve effect in health technologies. Lessons from the nonclinical literature.

INTRODUCTION: Many health technologies exhibit some from of learning effect, and this represents a barrier to rigorous assessment. It has been shown that the statistical methods used are relatively crude. Methods to describe learning curves in fields outside medicine, for example, psychology and engineering, may be better. METHODS: To systematically search non-health technology assessment literature (for example, PsycLit and Econlit databases) to identify novel statistical techniques applied to learning curves. RESULTS: The search retrieved 9,431 abstracts for assessment, of which 18 used a statistical technique for analyzing learning effects that had not previously been identified in the clinical literature. The newly identified methods were combined with those previously used in health technology assessment, and categorized into four groups of increasing complexity: a) exploratory data analysis; b) simple data analysis; c) complex data analysis; and d) generic methods. All the complex structured data techniques for analyzing learning effects were identified in the nonclinical literature, and these emphasized the importance of estimating intra- and interindividual learning effects. CONCLUSION: A good dividend of more sophisticated methods was obtained by searching in nonclinical fields. These methods now require formal testing on health technology data sets.

Biomedical Technology↗
PubMedDate not supplied

Focus on: Biomedical Engineering Department, Saint Therese Medical Center.

This paper describes the Biomedical Engineering Department at Saint Therese Medical Center, Waukegan, Illinois. The medical center is part of the Saint Therese Human Services Corporation. Two Biomedical Equipment Technicians service 845 pieces of electronic medical equipment. Within the first year, services were expanded so that the department provides a technical resource for the clinical departments within the center. The department's programs have resulted in cost savings through management of technology. The biomedical program is dedicated to the improvement of patient care and safety through the management of technology.

Biomedical Engineering↗
PubMed2000

Assessment of the learning curve in health technologies. A systematic review.

OBJECTIVE: We reviewed and appraised the methods by which the issue of the learning curve has been addressed during health technology assessment in the past. METHOD: We performed a systematic review of papers in clinical databases (BIOSIS, CINAHL, Cochrane Library, EMBASE, HealthSTAR, MEDLINE, Science Citation Index, and Social Science Citation Index) using the search term "learning curve." RESULTS: The clinical search retrieved 4,571 abstracts for assessment, of which 559 (12%) published articles were eligible for review. Of these, 272 were judged to have formally assessed a learning curve. The procedures assessed were minimal access (51%), other surgical (41%), and diagnostic (8%). The majority of the studies were case series (95%). Some 47% of studies addressed only individual operator performance and 52% addressed institutional performance. The data were collected prospectively in 40%, retrospectively in 26%, and the method was unclear for 31%. The statistical methods used were simple graphs (44%), splitting the data chronologically and performing a t test or chi-squared test (60%), curve fitting (12%), and other model fitting (5%). CONCLUSIONS: Learning curves are rarely considered formally in health technology assessment. Where they are, the reporting of the studies and the statistical methods used are weak. As a minimum, reporting of learning should include the number and experience of the operators and a detailed description of data collection. Improved statistical methods would enhance the assessment of health technologies that require learning.

Biomedical Technology↗