Patient and hub services represent a significant and often ineffective spend surrounding overall patient support. How can we use data and analytics to build better patient services programs that predict next best actions and achieve improved patient engagement and outcomes across the treatment journey? As the industry continues its shift to value-based care, this challenge has never been timelier.
Following our recent white paper, Patient Switching Behaviors Impact on Adherence and Engagement: A Predictive Analytics and Machine Learning Approach to Improving Hub Performance and Patient Outcomes, we committed to sharing our methodology.
The lifecycle of a patient prediction model follows a five-step process in setting up a successful patient switch prediction model:
Fill out the form below to download the methodology.
Brigham is a highly regarded speaker and thought leader on the value of data and analytics across the life sciences, pharma, and overall healthcare sector. His background is in data science and artificial intelligence…