Browsing by Subject "Design Science"

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    Mitigating Adverse Outcomes in Health and Wellness with Data Analytics: Investigation of Medical Device Recalls and Digital Exercise Program Churns
    (2024-03) Zhu, Yi
    The adverse outcomes in health and wellness, such as the failure of medical devices and the prevalence of human sedentary lifestyles, pose significant risks to patient safety, public health, and the financial stability of healthcare institutions and firms. To enhance proactive measures against these adverse outcomes and mitigate their potential harm, this dissertation employs empirical methodologies to investigate two types of adverse outcomes in health and wellness. Moreover, it also develops algorithmic approaches for predicting these outcomes. Specifically, this dissertation comprises three essays: the first essay addresses medical device recalls by proposing a design-science-based framework for predicting such recalls. This framework demonstrates superior performance compared to traditional predictive models and offers various insights for improving medical device safety regulations through different prediction problem setups. The second and third essays delve into human physical activity behaviors as recorded by digital exercise platforms. They explore the social contagion effect of churn digital exercise programs, examine the variations in the contagion effects based on individuals’ characteristics, and interpret these effects using complex contagion theory. An advanced deep learning approach is introduced to forecast exercise patterns indicative of potential digital activity program churn, leveraging the relationships between different exercise types and measures. The effectiveness of this approach is validated through experiments with both simulated and actual data, showcasing its advantages over traditional models. This dissertation provides substantial practical implications and contributes to the advancement of knowledge across the domains of information systems, healthcare, and wellness. Insights from the first essay provide critical policy recommendations for enhancing medical device safety, augmenting the understanding of predictive health information system design, and fostering data-driven methodologies for early detection of product recalls. The findings from the second and third essays emphasize the significance of leveraging social strategies to reduce churn in digital exercise programs and highlight the advantages of recommending users personalized exercise programs based on more accurate predictions of exercisers’ activities. These tailored programs aim to boost exercise adherence, thereby enhancing the overall personal wellness of exercisers. Overall, this dissertation enhances our understanding and prediction of medical device recalls and digital exercise program churn, elevating personal health and wellness outcomes.