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The Impact of Personality on Users' Specific Privacy Concerns Regarding Personal Health Information

The increased use of health information technology has made a wide range of personal health information available for practitioners and researchers alike. Notably, as more personal health information becomes available, there is increasing concern for the data's privacy. Personal electronic health information, i.e., digital report(s) of real-time patient-centered information, are a relatively new phenomenon for many of us to confront these days. Prior studies have examined the construct of privacy in-depth including cross-cultural perspective [1], analysis at different levels - organizational, group, and individual [2], literature reviews [3, 2] and more. Expounding on these areas, research is now beginning to investigate the influence personal characteristics has on privacy. Rather than viewing privacy concerns as a single multidimensional consequence, our study examines the link between personality traits and individual privacy concern dimensions. A widely established proxy for privacy [4], known as the concern for information privacy (CFIP), consists of four components: collection, errors, unauthorized secondary use and improper access. Unlike previous studies, we treat each of the privacy concern dimensions separately to determine how personality affects each specific concern. For some personality traits, we expect there to be a significant effect across all four dimensions; in others, the effect on some dimensions is expected to be significantly higher than others. In healthcare, the appropriate control of the information is viewed as an obligation of health care professionals from an ethics perspective but also as a function of their expertise, power and professional status [5]. We expect that characteristics such as the dynamic nature of healthcare and the private and intimate essence of patient information will highlight explicable relationships between personality traits and dimensions of privacy. We have developed a Likert-scale-based survey instrument corresponding to the model, gathered data, and are currently analyzing the dataset using structural equation modeling. We hope that the results of our study (once completed) will be beneficial for subsequent studies as well as for practitioners interfacing with concerned users of health data.