The company was looking to identify “high-risk” members in order to provide support services prior to a medical event occurring. The problem was they had very limited claims data due to the member type and newness to the plan. Health insurance companies create a variety of models, or forecasts, to improve services, expand care, minimize costs, and identify at risk individuals. Emory’s approach was to blend the desire for improved care with the goal of reducing costs to the plan.
Working together, Emory Solutions provided a subject matter expert on medical billing codes as well as created an extensive dataset from public data to cluster and identify potential “high risk” individuals. The model looked at existing claims, location, income, and numerous other variables to provide a probability of future event score. Individuals with a higher score could then receive additional support to ensure they are aware of services and taking the necessary precautions. Preventing future health events has the potential to not only save lives but also to save millions of dollars in plan expenses.
