• Health Policy and Management
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• v.10 no.3
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• pp.155-168
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• 2000

The SERVQUAL scale is based on the gap theory, which indicates the difference between consumers' expectations and their actual performance. In SERVQUAL scale, the expectations are defined as a "feasible ideal point"(ex, An Excellent hospital has up-to-date equipment). But empirical research identified important problems concerning the conceptual definitions of expectations. They suggests the usage of "desired expectations". Desired expectations are defined as the level at which the consumer predict the service that the organization they visited will perform(ex, $\bigcirc\bigcirc$ hospital has up-to-date equipment). The purpose of this study was to compare the feasible ideal point expectations with desired expectations in assessment of consumer expectations using SERVQUAL scale. We developed two types of questionnaires : (1) to measure feasible ideal point expectations, (2) to measure desired expections. Questionnaire were distributed to ambulatory patients who used the medical service. Total 329 patients participated the hosiptal satisfaction questionnaire(167 for feasible ideal point expectations, 162 for desired expectations). The major finding is as follows: (1) the SERVQUAL scale which was computed by the feasible ideal point showed the higher explanatory power in consumer satisfaction ($R^2$=0.26) than the other identified alternatives(desired expectation, $R^2$=0.11) The results of a study suggests that the feasible ideal point were more conceptually suitable to assess of consumer satisfaction using SERVQUAL scale.SERVQUAL scale.

• Journal of Digital Convergence
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• v.19 no.5
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• pp.263-268
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• 2021

The purpose of this study was to propose a model which is suitable for the actual delivery system by designing a fetal delivery hospital operation management and fetal health classification model. The number of deaths during childbirth is similar to the number of maternal mortality rate of 295,000 as of 2017. Among those numbers, 94% of deaths are preventable in most cases. Therefore, in this paper, we proposed a model that predicts the health condition of the fetus using data like heart rate of fetuses, fetal movements, uterine contractions, etc. that are extracted from the Cardiotocograms(CTG) test using a random forest. If the redundancy of the data is unbalanced, This proposed model guarantees a stable management of the fetal delivery health management system. To secure the accuracy of the fetal delivery health management system, we remove the outlier which embedded in the system, by setting thresholds for the upper and lower standard deviations. In addition, as the proportion of the sequence class uses the health status of fetus, a small number of classes were replicated by data-resampling to balance the classes. We had the 4~5% improvement and as the result we reached the accuracy of 97.75%. It is expected that the developed model will contribute to prevent death and effective fetal health management, also disease prevention by predicting and managing the fetus'deaths and diseases accurately in advance.