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Visualized Decision Making: Development and Application of Information Visualization Techniques to Improve Decision Quality of Nursing Home Choice

Choosing a nursing home for a close family member is a very crucial decision. In order to help consumers' decision making, several initiatives have resulted in public Web sites designed to share a set of collected quality indicators for nursing homes. However, it has been noted that the majority of consumers fail to fully utilize this quality information for various reasons. Often, inherent features of the data, including multidimensionality, complexity, and uncertainty, render use of the data by typical consumers very challenging. Information visualization (InfoVis) techniques have recently emerged that could alleviate some of these problems that consumers experience when utilizing complex datasets to aid in decision making of this type. However, several unsuccessful attempts in applying InfoVis to decision making suggest that a thorough understanding of the user's perspective is essential.

Accordingly, this dissertation will develop an InfoVis tool for the decision domain of choice of a nursing home. First, a framework of overarching InfoVis and decision theories, called "visualized decision making (VDM)" framework, will be developed and contextualized within the selection of a nursing home. Second, prototypes of InfoVis techniques will be designed for application within the framework, and the designed InfoVis technique will be implemented and empirically tested. Third, design guidelines for the use of InfoVis techniques for decision support will be developed based on lessons learned during the dissertation.

The results of this study will aid individuals who are faced with the decision of selecting a nursing home make a more informed decision more quickly. It is also believed that the results of this dissertation will be an interesting example for other designers of InfoVis techniques, as several issues faced in the context of choosing a nursing home can be generalized to other decision making contexts.

Committee Members:
Dr. Stephen E. Cross, GTRI, GT
Dr. Mary Czerwinski, Microsoft Research
Dr. John T. Stasko (Chair), CoC, GT
Dr. Gregory D. Abowd, CoC/HSI, GT
Dr. Brani Vidakovic, ISyE/BME, GT

Principal Investigator: Ji Soo Yi, Ph.D.

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