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Patient Switching Behaviors Impact on Adherence and Engagement: A Predictive Analytics and Machine Learning Approach to Improving Hub Performance and Patient Outcomes
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"Patient Switching Behaviors Impact on Adherence and Engagement: A Predictive Analytics and Machine Learning Approach to Improving Hub Performance and Patient Outcomes"
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Today we have access to more data, from more sources than we could ever dream possible. Living in a digital world, we increasingly need the ability to efficiently and effectively process this data for insights and actions in order to be competitive. The life sciences industry can leverage this data using analytic tools and machine learning to rapidly identify patient behaviors and patterns - allowing us to predict “next best actions” in our quest to improve patient outcomes.

This white paper discusses how predictive analytics and machine learning have the potential to transform healthcare by helping us identify diseases faster, decrease costs through precision therapies, improve clinical trial enrollment, and increase operational effectiveness.

Download now to learn how:

  • Technology can drive the success of patient services Hub programs operations
  • Data can be used to predict the next best action and increase the effectiveness of keeping patients on therapy
  • Predictive modeling identifies new ways to improve and manage patient outcomes and treatment pathways
  • Demonstrate the value of hub performance and measurable economic value


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