- Dimensions of Consumer Ratings of a Hospital Outpatient Service Quality.
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Ki Tae Moon, Seung Hum Yu, Woo Hyun Cho, Dong Kee Kim, Yunwhan Lee
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Korean J Prev Med. 2000;33(4):495-504.
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 Abstract
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- OBJECTIVES
To examine various dimensions of consumer ratings of health care service with factor analysis and to find which factors influence the overall quality of health care service. METHODS: A cross-sectional study was conducted on outpatients of a general hospital located in Sungnam City. A self-administered questionnaire was used to assess the consumer? ratings of health care service received. The response rate was 92.8% with a total of 537 persons completing the questionnaire. Factor analysis was performed on 34 items evaluating the quality of health care service. Items were grouped into 5 dimensions as a result of factor analysis and the reliability and validity of influence on patient service assessment were evaluated for each dimension. RESULTS: The 5 dimensions were as follows ; 1) physician services, 2) non-physician services, 3) process 4) facilities, and 5) cleanliness. A positive correlation with the quality of health care service was found for the dimensions of non-physician services and process, while no significant correlation was found for the dimensions of physician services, facilities, and cleanliness. CONCLUSIONS: The result of this study may provide basic information for the development of future self-administered questionnaires of consumer ratings and for the evaluation of quality improvement activities in hospital outpatient settings.
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Summary
- Statistical Methods for Multivariate Missing Data in Health Survey Research.
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Dong Kee Kim, Eun Cheol Park, Myong Sei Sohn, Han Joong Kim, Hyung Uk Park, Chae Hyung Ahn, Jong Gun Lim, Ki Jun Song
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Korean J Prev Med. 1998;31(4):875-884.
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Abstract
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- Missing observations are common in medical research and health survey research. Several statistical methods to handle the missing data problem have been proposed. The EM algorithm (Expectation-Maximization algorithm) is one of the ways of efficiently handling the missing data problem based on sufficient statistics. In this paper, we developed statistical models and methods for survey data with multivariate missing observations. Especially, we adopted the Em algorithm to handle the multivariate missing observations. We assume that the multivariate observations follow a multivariate normal distribution, where the mean vector and the covariance matrix are primarily of interest. We applied the proposed statistical method to analyze data from a health survey. The data set we used came from a physician survey on Resource-Based Relative Value Scale(RBRVS). In addition to the EM algorithm, we applied the complete case analysis, which used only completely observed cases, and the available case analysis, which utilizes all available information. The residual and normal probability plots were evaluated to access the assumption of normality. We found that the residual sum of squares from the EM algorithm was smaller than those of the complete-case and the available-case analyses.
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