Using Machine Learning and Big Data to Drive Patient Engagement and Better Health Outcomes

For many years, companies in the retail, telecom, insurance, and banking industries have used machine learning (ML) techniques to analyze terabytes of real-time data representing a wide range of customer interactions (across all channels), demographic characteristics, and lifestyle events. This session will explain how CIGNA has leveraged some of the ML techniques used to influence consumer behavior in other industries for their own purpose of influencing consumer behaviors towards lower medical costs and better healthcare outcomes. One example to be discussed is how they used a combination of claims, demographic, lab, call center, and click-stream data from web-interactions and mobile phone interactions to improve the timing, channel, and content they use to engage members with chronic conditions in coaching that lowers medical costs and improves healthcare outcomes for those patients.

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