You are what you eat: Using consumer data to predict health risk

  • Print
  • Connect
  • Email
  • Facebook
  • Twitter
  • LinkedIn
  • Google+
By Chris Stehno, Craig Johns | 01 January 2006
Medical predictive modeling can make use of massive consumer datasets that were once only used for marketing and sales activities for a variety of new applications, including the identification of an individual's health risks. The analysis of lifestyle-based data, otherwise known as lifestyle-based analytics, offers enormous promise to patients, doctors, and health and life insurers. Better prediction of lifestyle-based diseases is essential, as these diseases account for more than 70 percent of the disease in the United States today and represent 75 percent of the total medical dollars spent. Existing and widely available consumer data reflect individuals' lifestyles and can be analyzed with an eye toward early disease detection—offering a significant advantage in predicting diseases that are not hereditary and may not have any obvious medical precursors. This article describes how new lifestyle-based datasets and predictive modeling techniques are proving highly effective and how their use offers companies a significant competitive advantage.