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Kaohsiung, Taiwan

Chang C.-S.,ROC Naval Academy | Chen S.-Y.,Kaohsiung Medical University | Lan Y.-T.,I - Shou University
BMC Health Services Research

Background: Interaction between service provider and customer is the primary core of service businesses of different natures, and the influence of trust on service quality and customer satisfaction could not be ignored in interpersonal-based service encounters. However, lack of existing literature on the correlation between service quality, patient trust, and satisfaction from the prospect of interpersonal-based medical service encounters has created a research gap in previous studies. Therefore, this study attempts to bridge such a gap with an evidence-based practice study. Methods. We adopted a cross-sectional design using a questionnaire survey of outpatients in seven medical centers of Taiwan. Three hundred and fifty copies of questionnaire were distributed, and 285 valid copies were retrieved, with a valid response rate of 81.43%. The SPSS 14.0 and AMOS 14.0 (structural equation modeling) statistical software packages were used for analysis. Structural equation modeling clarifies the extent of relationships between variables as well as the chain of cause and effect. Restated, SEM results do not merely show empirical relationships between variables when defining the practical situation. For this reason, SEM was used to test the hypotheses. Results: Perception of interpersonal-based medical service encounters positively influences service quality and patient satisfaction. Perception of service quality among patients positively influences their trust. Perception of trust among patients positively influences their satisfaction. Conclusions: According to the findings, as interpersonal-based medical service encounters will positively influence service quality and patient satisfaction, and the differences for patients' perceptions of the professional skill and communication attitude of personnel in interpersonal-based medical service encounters will influence patients' overall satisfaction in two ways: (A) interpersonal-based medical service encounter directly affects patient satisfaction, which represents a direct effect; and (B) service quality and patient trust are used as intervening variables to affect patient satisfaction, which represents an indirect effect. Due to differences in the scale, resources and costs among medical institutions of different levels, it is a most urgent and concerning issue of how to control customers' demands and preferences and adopt correct marketing concepts under the circumstances of intense competition in order to satisfy the public and build up a competitive edge for medical institutions. © 2013 Chang et al.; licensee BioMed Central Ltd. Source

Wang C.-H.,Chung Cheng Institute of Technology | Huang C.-H.,ROC Naval Academy
RAIRO - Operations Research

This study aims at the multi-state degraded system with multi-state components to propose a novel approach of performance evaluation and a preventive maintenance model from the perspective of a system's components. The general non-homogeneous continuous-time Markov model (NHCTMM) and its analogous Markov reward model (NHCTMRM) are used to quantify the intensity of state transitions during the degradation process. Accordingly, the bound approximation approach is applied to solve the established NHCTMMs and NHCTMRMs, thus evaluating system performance including system availability and total maintenance cost to overcome their inherent computational difficulties. Furthermore, this study adopts a genetic algorithm (GA) to optimize a proposed preventive maintenance model. A simulation illustrates the feasibility and practicability of the proposed approach. © EDP Sciences, ROADEF, SMAI 2015. Source

Lei P.-R.,ROC Naval Academy
Knowledge and Information Systems

Rapid growth in location data acquisition techniques has led to a proliferation of trajectory data related to moving objects. This large body of data has expanded the scope for trajectory research and made it applicable to a more diverse range of fields. However, data uncertainty, which is naturally inherent in the trajectory data, brings the challenge in trajectory data mining and affects the quality of the results. Specifically, unlike trajectory collected from vehicles moving along road networks, trajectory data generated by vessels moving free in maritime space have increased the difficulty of sea traffic analysis and anomalous behavior detection. Furthermore, due to the huge volume and complexity of maritime trajectory data, it is hard to define the abnormality of movement behavior and detect anomalies. Additionally, traditional analysis and evaluation by human intelligence is overloaded with the dramatic increasing in amount of maritime trajectory data and is an inefficient approach. Thus, an effective automated method for mining trajectory data and detecting anomalies would be a valuable contribution to maritime surveillance. This paper explores the maritime trajectory data for anomalous behavior detection. We propose a framework for maritime trajectory modeling and anomaly detection, called MT-MAD. Our model takes into account the fact that anomalous behavior manifests in unusual location points and sub-trajectories in the spatial domain as well as in the sequence and manner in which these locations and sub-trajectories occur. This study therefore began by identifying outlying features required for anomaly detection, including spatial, sequential, and behavioral features. We then explore the movement behavior from historical trajectories and build a maritime trajectory model for anomaly detection. The proposed model accurately describes movement behavior and captures outlying features in trajectory data. We then developed an anomaly detection algorithm based on this model in which an indicator is used to evaluate suspicious behavior and scores trajectory behavior according to the defined outlying features. Experiment results demonstrate that the proposed MT-MAD framework is capable of effectively detecting anomalies in maritime trajectories. © 2015 Springer-Verlag London Source

Chang C.-S.,ROC Naval Academy | Chen S.-Y.,Kaohsiung Medical University | Lan Y.-T.,I - Shou University
BMC Medical Informatics and Decision Making

Background: No previous studies have addressed the integrated relationships among system quality, service quality, job satisfaction, and system performance; this study attempts to bridge such a gap with evidence-based practice study. Methods. The convenience sampling method was applied to the information system users of three hospitals in southern Taiwan. A total of 500 copies of questionnaires were distributed, and 283 returned copies were valid, suggesting a valid response rate of 56.6%. SPSS 17.0 and AMOS 17.0 (structural equation modeling) statistical software packages were used for data analysis and processing. Results: The findings are as follows: System quality has a positive influence on service quality (γ§ssub§11§esub§= 0.55), job satisfaction (γ§ssub§21§esub§= 0.32), and system performance (γ§ssub§31§esub§= 0.47). Service quality (β§ssub§31§esub§= 0.38) and job satisfaction (β§ssub§32§esub§= 0.46) will positively influence system performance. Conclusions: It is thus recommended that the information office of hospitals and developers take enhancement of service quality and user satisfaction into consideration in addition to placing b on system quality and information quality when designing, developing, or purchasing an information system, in order to improve benefits and gain more achievements generated by hospital information systems. © 2012 Chang et al.; licensee BioMed Central Ltd. Source

Lei P.-R.,ROC Naval Academy
IEEE ISI 2013 - 2013 IEEE International Conference on Intelligence and Security Informatics: Big Data, Emergent Threats, and Decision-Making in Security Informatics

As security requirements in coastal water and sea ports, maritime surveillance increases the duty. In this research, we focus on the maritime trajectory data to explore movement behavior for anomaly detection in maritime traffic. Trajectory data records the moving objects' true movement and provides the opportunity to discover the movement behavior for anomaly detection. The multidimensional outlying features are first identified and defined. To deal with the uncertain property of trajectory, a maritime trajectory modeling is developed to explore the movement behavior from historical trajectories and build a maritime trajectory model for anomaly detection. Then, our ongoing work is developing an anomaly detection algorithm to detect anomalous moving objects from real time maritime trajectory stream effectively. This work should contribute the area of maritime security surveillance by trajectory data mining. © 2013 IEEE. Source

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