DSaaS (Data Science as a Service)

A top healthcare insurance company had a long list of priorities and needed additional capacity and expertise on their Data Science team. Emory was engaged to provide a “surge” capacity and tackle one of their highest priority items.


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.

Key Outcomes and Metrics

Developed and deployed (internally) a model for the company to use for assessing risk of members and providing targeted care to improve service and outcome.

Technical Details

  • Cloud & open source tools to allow scalability, mass data storage, with a low pay-as-you go cost model
  • Naives bayes and K-nearest Neighbors classification models
Your team allowed us to move quicker and more strategic.

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