Personal Health Management and Technologies
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ItemA Lazy User Perspective to the Voluntary Adoption of Electronic Personal Health Records (PHRs)( 2018-01-03)Personal Health Records (PHRs) have been imbued with the potential to improve health outcomes for individual healthcare consumers, providers, and the broader healthcare system. With Meaningful Use Stage 2 now mandating the implementation of tethered PHRs, tethered to provider electronic health records (patient portals), will healthcare consumers voluntarily use PHRs and contribute to safety, quality, efficiency and reduced health disparities through engagement? Or will PHR use remain low? In this qualitative study, using grounded theory, we asked users how they currently managed their personal health information (PHI) and why. Using the lazy user model, we found that letting physicians manage healthcare consumers PHI is the least effort-based solution and thus the predominant and preferred solution. Providers as guardians of patient PHI suggests the low use rates may persist yet. We should do more to make these technologies usable and accessible to those with irregular contact with a primary care physician.
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ItemDesign of a Mobile-App for Non-Invasively Detecting High Blood Cholesterol Using Eye Images( 2018-01-03)Medical research proves that high blood cholesterol is identifiable through the iris portion of the human eyes. Currently, a clinical blood test is the only method used in identifying high cholesterol. This study proposed an IT-based approach to prove the concept of monitoring cholesterol content in the blood using the human irises. We have developed a conceptual framework and a smartphone application aimed at capturing an image of the eye and analyzing it for symptoms associated with high blood cholesterol. This is a break-through innovation for a non-invasive way of finding out blood-cholesterol. The application captures the eye image and necessary metadata from a user. Then, a software code will examine the textural pattern of the eye, thus detecting the presence of high blood cholesterol. The result will be displayed on the user's mobile application. Primarily, the feedback to the user will be either "No" or "Yes" cholesterol content in the blood. The results presented through this app will form the basis for a further medical checkup if necessary.
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ItemSelfies of Twitter Data Stream through the Lens of Information Theory: A Comparative Case Study of Tweet-trails with Healthcare Hashtags( 2018-01-03)Little research in information system has been carried out on the subject of user’s choice of different components when composing a tweet through the analytical lens of information theory. This study employs a comparative case study approach to examine the use of hashtags of medical-terminology versus lay-language in tweet-trails and (1) introduces a novel H(x) index to reveal the complexity in the statistical structure and the variety in the composition of a tweet-trail, (2) applies radar graph and scatter plot as intuitive data visualization aids, and (3) proposes a methodological framework for structural analysis of Twitter data stream as a supplemental tool for profile analysis of Twitter users and content analysis of tweets. This systematic framework is capable of unveiling patterns in the structure of tweet-trails and providing quick and preliminary snap shots (selfies) of Twitter data stream because it’s an automatic and objective approach which requires no human intervention.
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ItemPromoting Use of Patient-Centered Health IT: Assessment and Ranking of Incentive Mechanisms( 2018-01-03)The health care domain is undergoing a sweeping shift from a model of paternalism towards increased patient-centered care. Vendors offering patient-centered health IT use incentive mechanisms to motivate the continued use of health IT. However, incentive mechanisms may not always be beneficial to patient-centered care and may lack focus on actual treatment processes. Therefore, we focus on the research question: What incentive mechanisms are or are not useful for promoting use of patient-centered health IT and why? We assess and rank 28 incentive mechanisms by utility for patient-centered health IT. Findings reveal that reminders and interface improvements are most beneficial and that social comparison and social facilitation mechanisms are most detrimental to patient-centered care. This work extends the scientific knowledge base on patient-centered health IT, establishes a foundation for future research on patient-centered incentive mechanisms, and provides practical audiences with insights on how to effectively design patient-centered health IT.
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ItemWhy patients switch help from online to offline: An empirical investigation of emotions and effects of cognitions( 2018-01-03)People have different reactions when they encounter different levels of health threats. Individual cognitions towards the seriousness of health threat and emotions arise from health threats often motivate people to seek help online and offline. To examine the effects of cognitions and emotions on help seeking behaviors on different levels of health threats, we propose a research model based on protection motivation theory and cognition-emotion-behavior framework. Empirical data were collected through questionnaires and analyzed by Smart PLS. The results show that perceived susceptibility and perceived severity have positive significant influence on fear which leads on anxiety. Perceived health knowledge has negative influence on anxiety. Fear and anxiety can motivate individuals to seek help online and offline, especially in the high health threats condition. In addition, fear has significant influence on help seeking offline while anxiety has significant influence on help seeking online. Expectation confirmation has different effects on help seeking offline in different levels of health threats. This study extends PMT through the lens of cognition-emotion-behavior framework, and it expands research scope to help seeking behaviors.
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ItemHow Does Self-Tracking Go? A Research Model and Pre-Test( 2018-01-03)Physical inactivity has become a serious problem in modern societies leading to a multitude of diseases. Insurer try to counteract this problem by supporting the use of self-tracking applications. While the effectiveness of self-tracking applications is widely assumed, scant studies investigate the influence of self-tracking applications and those few studies show different results. We propose a research model and measurements based on the cognitive dissonance theory to explain how and why self-tracking influences behavior. This understanding is of critical importance for the design of effective self-tracking applications. Specifically, we propose that the usage of step-counter apps leads to a higher awareness of two inconsistent cognitions, which induce cognitive dissonance. Because people strive for consistency, they try to reduce the dissonance through either ignoring the situation, finding new information or behavior change. We tested our measurements with an item-sort-task and found high substantive validity as an indicator for good construct validity.
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