ABSTRACT
RATIONALE AND OBJECTIVES: To evaluate a model for predicting technological obsolescence of computed tomography (CT) equipment. MATERIALS AND METHODS: Baseline data consisted of various models of CT scanners that have been on the market since 1974 and represent a technological leap in CT. In documenting the CT scans, a principal component analysis was performed to reduce the number of variables. A Cox regression model was used to calculate the probability of a technology leap. RESULTS: The CT parameters were divided into three main components: detection system, image resolution, and device performance. Cox regression odds ratios show that a technology leap can be expected as a function of the variables device power (1.457), detection system (0.818), and image resolution (0.964). CONCLUSION: Our results show that the variables that predict the technological leap in CT are device performance, image resolution, and detection system. The results provide a better understanding of the expected technological changes in CT, which will lead to advances in planning investments in this technology, purchasing and installing equipment in hospitals where this type of technology is not yet available, and renewing the technological base already installed.
Subject(s)
Technology , Tomography, X-Ray Computed , Humans , Equipment Design , Tomography Scanners, X-Ray Computed , HospitalsABSTRACT
INTRODUCTION: In recent years, several hospitals have incorporated MRI equipment managed directly by their cardiology departments. The aim of our work is to determine the total cost per test of both CT and MRI in the setting of a Cardiology Department of a tertiary hospital. MATERIALS AND METHODS: The process followed for estimating the costs of CT and MRI tests consists of three phases: (1) Identification of the phases of the testing process; (2) Identification of the resources consumed in carrying out the tests; (3) Quantification and assessment of inputs. RESULTS: MRI involves higher personnel (EUR 66.03 vs. EUR 49.17) and equipment (EUR 89.98 vs. EUR 33.73) costs, while CT consumes higher expenditures in consumables (EUR 93.28 vs. EUR 22.95) and overheads (EUR 1.64 vs. EUR 1.55). The total cost of performing each test is higher in MRI (EUR 180.60 vs. EUR 177.73). CONCLUSIONS: We can conclude that the unit cost of each CT and MRI performed in that unit are EUR 177.73 and EUR 180.60, respectively, attributable to consumables in the case of CT and to amortization of equipment and staff time in the case of MRI.
ABSTRACT
BACKGROUND: The relative lack of flexibility of parametric models has led to the development of nonparametric regression techniques based on the family of generalized additive models. However, despite the potential advantages of using Generalized Additive Model (GAM) in practice many models have, until now, not been sufficiently explored in health economics problems. It could be interesting to calculate a new flexible hospital production function by means of a GAM including interactions and to compare it with the classic model Cobb-Douglas in the prediction of the behavior of productive factors. METHOD: The flexible model considered has been the AM including the beds-facultative interaction. The covariates "Hospital", being a categorical variable and "Year" being a continuous variable, have also been included in the model. Based on the estimation of the model penalized thin plate splines will be used to represent smoothed functions. In this configuration, the smoothed parameters will be estimated via REML. RESULTS: Cobb-douglas model fits well for the production functions of the more general clinical and surgical services, while the GAM adjusts better in the case of more specialized medical services. CONCLUSIONS: Generalized Additive Models are more flexible than parametric models, providing a better fit in the presence of non-linear relationships and thus allowing more accurate prediction values. The results of this study suggest that AM is a promising technique for the areas of research and application in health economics.
