Activity cost analysis: a tool to cost medical services and improve quality of care.

This paper suggests an activity-based cost (ABC) system as the appropriate cost accounting system to measure and control costs under the microstatistical episode of care (EOC) paradigm suggested by D. W. Emery (1999). ABC systems work well in such an environment because they focus on activities performed to provide services in the delivery of care. Thus, under an ABC system it is not only possible to accurately cost episodes of care but also to more effectively monitor and improve the quality of care. Under the ABC system, costs are first traced to activities and then traced from the activities to units of episodic care using cost drivers based on the consumption of activity resources.

Ultrasound image texture analysis for characterizing intramuscular fat content of live beef cattle.

The primary factors in determining beef quality grades are the amount and distribution of intramuscular fat percentage (IMFAT). Texture analysis was applied to ultrasound B-mode images from ribeye muscle of live beef cattle to predict its IMFAT. We used wavelet transform (WT) for multiresolutional texture analysis and second-order statistics using a gray-level co-occurrence matrix (GLCM) technique. Sets of WT- and GLCM-based texture features were calculated from ultrasonic images from 207 animals and linear regression methods were used for IMFAT prediction. WT-based features included energy ratios, central moments of wavelet-decomposed subimages and wavelet edge density. The regression model using WT features provided a root mean square error (RMSE) of 1.44 for prediction of IMFAT using validation images, while that of GLCM features provided an RMSE of 1.90. The prediction models using the WT features showed potential for objective quality evaluation in the live animals.

Frequency invariant classification of ultrasonic weld inspection signals.

Automated signal classification systems are finding increasing use in many applications for the analysis and interpretation of large volumes of signals. Such systems show consistency of response and help reduce the effect of variabilities associated with human interpretation. This paper deals with the analysis of ultrasonic NDE signals obtained during weld inspection of piping in boiling water reactors. The overall approach consists of three major steps, namely, frequency invariance, multiresolution analysis, and neural network classification. The data are first preprocessed whereby signals obtained using different transducer center frequencies are transformed to an equivalent reference frequency signal. Discriminatory features are then extracted using a multiresolution analysis technique, namely, the discrete wavelet transform (DWT). The compact feature vector obtained using wavelet analysis is classified using a multilayer perceptron neural network. Two different databases containing weld inspection signals have been used to test the performance of the neural network. Initial results obtained using this approach demonstrate the effectiveness of the frequency invariance processing technique and the DWT analysis method employed for feature extraction.