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J Korean Surg Soc. 2011 Jun;80(6):373-383. English. Review. https://doi.org/10.4174/jkss.2011.80.6.373
Hong DP , Song J .
Department of Service Systems Management & Engineering, Sogang University Graduate School of Business, Seoul, Korea. jaekisong@sogang.ac.kr
Area of Information Systems and Quantitative Sciences, Texas Tech University Rawls College of Business, Lubbock, TX, USA.
Abstract

PURPOSE: In Korean society, extra-large hospitals are congested with the majority of patients. Because of the congestions, the urgent patients need to wait anywhere from as short as a month to around three months. These concentrations of the patients on the extra-large hospitals causes not only the economic problem in terms of loss of opportunity cost and resources of other medium and large hospitals but also the fear and the consequential stress of the patients and the families of the patients who are waiting for the surgeries. The phenomenon of the concentrations derived due to the insufficient information to the medical consumers. If the information on medical treatment services such as surgery schedule is provided before the selection of hospital, we expect that the selection of hospital for the patients and their family will differ, resulting in redistribution of concentration phenomenon. In this paper, we propose and verify the effective distribution system for the concentration on the extra-large hospitals. METHODS: Web simulation survey was conducted. A total 100 respondents were divided into 4 groups of 25 respondents and the different information was provided to each group. RESULTS: Through multiple comparisons among groups, only group which was provided with both information about 'the difference of surgical results' and 'the waiting time for surgery', had difference in significance. CONCLUSION: By providing two sets of information to patients, reckless selection of extra-large hospitals can be spread to more appropriate hospitals and therefore achieve effective distribution of the population concentration on extra-large hospital.

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