67 research outputs found
Physicians Treating Physicians at the End of Life: The Relational Advantage in Treatment Choice
Get PDFThis study examines the agency problems by estimating the informational and relational effects of physician-patients on their invasive end-of-life treatment. To address potential issues of patient selection, we compare treatment intensity between physician- versus nonphysician-patients attended by the same doctor in the same hospital, and control for patients previous choices of doctors. To identify the relational effect, we further compare the impacts of physician-patients specializing in the same area as their attending doctors versus those in different areas. Physician-patients receive more care than comparable nonphysician-patients, and the increased volume results mostly from physician-patients relational advantages, not from their information advantages.JEL Classification Codes: D83, I11, J44The study received institutional review board approval from the Academia Sinica on Use of Humans as Experimental Subjects, AS-IRB-BM-13081 v5. We acknowledge funding from the JSPS Kakenhi Grant Number JP 17H02537 and the Academia Sinica Career Award.http://www.grips.ac.jp/list/jp/facultyinfo/chen-stacey/profile-s-chen
Computer-aided Diagnosis of Breast Elastography
Get PDFUltrasonography has been an important imaging technique for detecting breast tumors. As opposed tothe conventional B-mode image, the real-time tissue elastography by ultrasound is a new technique for imagingthe elasticity and applied to detect the stiffness of tissues. The red region of color elastography indicatesthe soft tissue and the blue one indicates the hard tissue. The harder tissue usually is classified as malignancy.In this paper, the authors proposed a computer-aided diagnosis( CAD) system on elastography tomeasure whether this system is effective and accurate to classify the tumor into benign and malignant. Accordingto the features of elasticity, the color elastography was transferred to hue, saturation, and value(HSV) color space and extracted meaningful features from hue images. Then the neural network was utilizedin multiple features to distinguish tumors. In this experiment, there are 180 pathology-proven cases including113 benign and 67 malignant cases used to examine the classification. The results of the proposedsystem showed an accuracy of 83.89 %, a sensitivity of 82.09 % and a specificity of 84.96 %. Compared withthe physician\u27s diagnosis, an accuracy of 78.33 %, a sensitivity of 53.73 % and a specificity of 92.92 %, theproposed CAD system had better performance. Moreover, the agreement of the proposed CAD system andthe physician\u27s diagnosis was calculated by kappa statistics, the kappa 0.64 indicated there is a fair agreementof observers
Physicians Treating Physicians at the End of Life: The Relational Advantage in Treatment Choice
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