Integrating Multivariate Method and Quality Function Deployment to Analyze In-patient Satisfaction

Background : The increasing competition in healthcareindustry has caused the delivery of service quality to patientsbecome essential. Every hospital competes to deliver the bestservice to its patients. As a result, it is necessary to analyzehospitalized patient satisfaction. This study discusses servicequality improvement in healthcare industry by analyzing inpatientsatisfaction using Multivariate Analysis and QualityFunction Deployment (QFD).Objectives: The objectives of this study are to identify patients'characteristics which are significantly affect their satisfactionlevel, to identify service attributes and dimensions which arecritical to patients, and subsequently improve those attributes.Method: The identification of characteristics and servicedimensions which are significantly affect patients' satisfactionlevel is accomplished using Multivariate Analysis. While thecritical service attributes identification is completed usingImportance-Performance Analysis. Afterward, using Houseof Quality (HOQ), as the basis of QFD, those critical serviceattributes are developed into service elements.Result: Using Discriminant Analysis, the result of this studyshows that patients' characteristics which significantly affecttheir satisfaction level are sex and occupation. The male andunemployed patients are more satisfied than the female andemployed patients. Afterward, Factor Analysis brings aboutfive new factors (service dimensions), which are the linearcombinations of the original 42 service attributes. Based onthe Importance-Performance Analysis, there are four serviceattributes which are critical to be improved which have highimportance level, but low performance level. Then, using theQuality Function Deployment (QFD), the four critical serviceattributes are developed into service elements. The serviceelements with high priorities are training program, recruitmentof experts, standard of information flow, online administrationsystem, and computer as provider of information.Conclusion: Service quality improvement in healthcareindustry can be analyzed more comprehensive by integratingMultivariate Method and Quality Function Deployment (QFD).The result of this study may provide contributions to hospitalsin general in enhancing its service performance to achieve itspatients' satisfaction

Adoption of Variable Rate Technology

Site Specific Management (SSM), which also variously referred to as Variable Rate Technology (VRT), is an emergingtechnology that enables producers to make more precise input application decisions based on soil and fieldcharacteristics. This study analyzes factors influencing the adoption of VRT for fertilizer application for cash grainproduction in Ohio. Results show that producer and field characteristics might influence the adoption decision onvarious SSM components differently. It also provides insight as to the sequence of adoption of SSM componenttechnologies and how this sequence might differ for producers of differing characteristics

An Economic Analysis of Variable Rate Technology

Variable Rate Technology (VRT) offers an opportunity to improve production efficiency by allowing input applicationsto fluctuate in response to spatial variations in soil characteristics and nutrient levels. Society may also benefit fromreduced negative externalities, such as surface and groundwater contamination, from input applications. Using adynamic spatial model, this study examines how the interaction among variability, spatial autocorrelation, and meanlevel of soil fertility affects optimal sampling density and the economic gains from VRT. VRT was found to beprofitable under selected conditions, and the optimal grid size will vary with these conditions. In the case wherevariability and mean fertility levels are significantly high associated with low spatial autocorrelation, VRT producesgreater net returns than Uniform Rate Technology (URT), even with the smallest grid size to base the input applicationdecisions. Results also demonstrate that optimal grid size increases with increased spatial autocorrelation

An Overview of the Use of Neural Networks for Data Mining Tasks

In the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks