• Korea Journal of Hospital Management
• /
• v.10 no.4
• /
• pp.75-97
• /
• 2005

The research objective is to empirically investigate the compensation packages maximizing the utilities of internal customers by applying the market segmentation theory. Data was collected from four Korean hospitals in Seoul, Busan and Gyunggi-do. The research is designed to seek the compensation package maximizing the utility of doctors by mixture regression model, which has been applied as latent structure and other type of finite mixture models from various academic fields since early 1980s. The mixture regression model shows the optimal segments number and fuzzy classification for each observation by EM(expectation-maximization algorism). The finite mixture regression model is to unmix the sample, to identify the groups, and to estimate the parameters of the density function underlying the observed data within each group. The doctors were segmented into 5 groups by their preference for the compensation package. The results of this study imply that the utility of doctors increases with differentiated compensation package segmented by their preference.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.27 no.5
• /
• pp.449-458
• /
• 2007

In this paper, we reconstructed the cross-sectional images of two phantoms simulating a petrochemical process from gamma radiation measurements. Three different weight functions for EM image reconstruction algorithm were built and compared with histograms representing the variance of the homogeneity of the phantom material, The radiation source, $^{137}Cs$, collimated by a lead with 5 mm diameter aperture and the measurement was made with a lead shielded 1inch NaI detector. As a result, the method taking into account the beam area in each pixel for a weight function showed the best resolution among the three methods.

• Health Policy and Management
• /
• v.12 no.2
• /
• pp.1-22
• /
• 2002

Since the early 1980s scholars have applied latent structure and other type of finite mixture models from various academic fields. Although the merits of finite mixture model are well documented, the attempt to apply the mixture model to medical service has been relatively rare. The researchers aim to try to fill this gap by introducing finite mixture model and segmenting inpatients DB from one general hospital. In section 2 finite mixture models are compared with clustering, chi-square analysis, and discriminant analysis based on Wedel and Kamakura(2000)'s segmentation methodology schemata. The mixture model shows the optimal segments number and fuzzy classification for each observation by EM(expectation-maximization algorism). The finite mixture model is to unfix the sample, to Identify the groups, and to estimate the parameters of the density function underlying the observed data within each group. In section 3 and 4 we illustrate results of segmenting 4510 patients data including menial and ratio scales. And then, we show AHP can be identify the attractiveness of each segment, in which the decision maker can select the best target segment.