Health Care Analytics: Modeling Behavioral Risk Factors Associated With Disease




Gritsenko-Toth, Elena

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Currently, there is a lot of excitement in the healthcare field about using big data and healthcare analytics for disease risk prediction, clinical decision support, and overall support for personalized medicine. However, this excitement hasn’t effectively translated to improved clinical outcomes due to knowledge gaps, a lack of behavioral risk models and resistance to evidence-based practice. Reportedly, only 10-20% of clinical decisions are known to be evidence-based (Moskowitz, McSparron, Stone, & Celi, 2015) and this problem is further highlighted by the fact that the U.S. spends more money on healthcare per person than any other nation, while still wrestling with poor health outcomes (Barrett, Humblet, Hiatt, & Adler, 2013). Critics say there are inadequate technological resources and analytical education for clinicians to make big data useful in the clinical world (Neff, 2013). Healthcare technology innovators often neglect important aspects of the reality of integrating clinical data into healthcare solutions (Neff, 2013). In response to these problems, this study examines big data and healthcare analytics for use in clinical applications and suggests HIM professionals develop behavioral risk factor prediction models to bridge the gap between data scientists and clinicians.



Healthcare, Analytics, Health information management, HIM, Behavioral risk factors


Gritsenko-Toth, E. (2019). <i>Health care analytics: Modeling behavioral risk factors associated with disease</i> (Unpublished thesis). Texas State University, San Marcos, Texas.


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