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Abstract: “Emergency medicine” is the front line of medical service a hospital provides; also it is the department people seek medical care from immediately after an emergency happens. The statistics by the Department of Health, Executive Yuan, indicate that over years, the number of people at the emergency department has been increasing. The US has introduced and practiced the triage system in the emergency medicine in 1960, whereby to aid the emergency department in allocating the patients, to give them appropriate medical care by the fast decision of the nurses and doctors in case of the patients’ seriousness through their judgment. This study takes on the knowledge contained in the massive data of unknown characteristics in the triage database at a Taiwanese regional hospital, using the cluster analysis and the rough set theory as tools for data mining to extract, with the analysis software ROSE2 (Rough Sets Data Explorer) and through rule induction technique, the imprecise, uncertain and vague information of rules from the massive database, and builds the model that is capable of simplifying massive data while maintaining the accuracy in classifying rules. After analyzing and evaluating the knowledge obtained from relevant mining in the hospitals past medical data for the consumption of emergency medical resources, this thesis proposes suggestions as reference for the hospitals in subsequent elevation of medical quality and decrease in operative costs. [Copyright &y& Elsevier]

Abstract: Managers in call centers use metrics to measure organizational performance; unfortunately, these metrics only reveal how well service agents process calls. To achieve substantial quality improvement, managers need to obtain in-depth information from operational metrics such as first call resolution (FCR). This research incorporated rough set theory (RST) in analyzing FCR to produce decision rules. RST has been known to reduce the number of attributes and attribute values without affecting the original results. Data from a tariff (industry segment) call center in Taiwan were used in this study, and a four-step process was used to produce the final decision rules. The results were verified by a 10-fold cross-validation process against the decision rules produced without applying RST. The decision rules produced with RST are as effective as those produced without, but with reduced number of attributes and attribute values. This reduced decision rule set can help managers analyze the current operator procedures more efficiently and subsequently improve the call center efficiency. Without affecting effectiveness, the RST application reduces the needed number of attributes and attribute values to produce a more compact decision rule set, and this increased efficiency, in turn, allows managers to delve deeper into the operational factors. [Copyright &y& Elsevier]

Applying competency models to identify and develop capabilities of civil servants is now a leading strategy for every government. However, an ideal competency model usually contains too many intended competencies, impeding implementation. Recently, some scholars and experts argued that there is a maximum of eight competencies for effective assessment. Hence, how to simplify a set of competencies becomes an important issue. This study is presented as a test case to extend practical applications of rough set theory (RST) in the human resource field of Government. A well-known data mining technique, RST is a relatively new approach to this problem and is good at data reduction in qualitative analysis. Hence, the rough set approach is suitable for dealing with the qualitative problem in simplifying a set of competencies. This paper slimmed a set of competencies using RST, thus helping the Taiwan Government to better understand the perceived competency levels of its civil servants. Using the rough set analysis, this paper successfully reduced the numerous essential competencies into a more compact set, by omitting low-consensus competencies. [ABSTRACT FROM AUTHOR]