In collaboration with Aetna’s Medicare Stars team, this project focuses on improving the way grievance volumes are predicted and managed. By combining structured member data with natural language processing (NLP) on complaint texts, we aim to build a forecasting model that helps Aetna anticipate volumes in grievances before they happen. With more accurate predictions, the team can better plan staffing, resolve issues faster, and ultimately improve member satisfaction and CMS Star Ratings. The insights and tools developed will also lay the groundwork for future optimization efforts.
Watch the team present this project at 02:06:36 in the session recording here.
Keywords: time series model, forecasting, healthcare, grievance volume prediction