Characteristics of personal health information management groups: findings from an online survey using Amazon’s mTurk

Sujin Kim, Jeffrey T. Huber

Abstract


Objective: The study characterized three groups with different levels of familiarity with personal health information management (PHIM) in terms of their demographics, health knowledge, technological competency, and information sources and barriers. In addition, the authors examined differences among PHIM groups in subjective self-ratings and objective test scores for health literacy.

Methods: A total of 202 survey participants were recruited using Amazon’s Mechanical Turk (mTurk) service, a crowdsourcing Internet service. Using K-means clustering, three groups with differing levels of familiarity with PHIM were formed: Advanced, Intermediate, and Basic.

Results: The Advanced group was the youngest, and the Basic group contained the highest proportion of males, whereas the Intermediate group was the oldest and contained the fewest males. The Advanced group was significantly more likely to engage in provider- or hospital-initiated PHIM activities such as emailing with providers, viewing test results online, and receiving summaries of hospital visits via email or websites than the other groups. The Basic group had significantly lower information management skills and Internet use than the other groups. Advanced and Basic groups reported significant differences in several information barriers. While the Advanced group self-reported the highest general literacy, they scored lowest on an objective health literacy test.

Conclusions: For effective personal health records management, it is critical to understand individual differences in PHIM using a comprehensive measure designed to assess personal health records–specific activities. Because they are trained to perform an array of information management activities, medical librarians or patient educators are well positioned to promote the effective use of personal health records by health consumers.

Keywords


Cluster Analysis; Health Literacy; Information Management; Personal Health Records

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DOI: https://doi.org/10.5195/jmla.2017.312

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Copyright (c) 2017 Sujin Kim, Jeffrey T. Huber

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