Predicting Shoppable Services Saves USD 5M in Medical Costs for a Leading Health Insurance Provider

Case Study

Predicting Shoppable Services Saves USD 5M in Medical Costs for a Leading Health Insurance Provider 

Business Objective

Our client is a major provider of health insurance plans in the US. The client expects member steerage to save significant medical costs and lower out-of-pocket expenses for members. They wanted to identify members with the likely need for a shoppable service(s) so that they can be steered towards cost-effective providers. More specifically, the client wanted to:

  • Predict members that are likely to have a shoppable procedure (or claim) in the next 30 days
  • Identify key drivers to enable member outreach team to intervene and steer members towards cost-effective procedures and providers

Challenges

  • Large set of data running into 30 million rows per month
  • Designing scripts for an outbound calling program, considering the involvement of stakeholders from multiple teams (digital engagement, member outreach, account management)

Solution Methodology 

  • Started with determining the top shoppable services that represent a sizeable opportunity for potential ROI – e.g., bariatric, musculoskeletal outpatient, ENT, imaging
  • Created hypotheses to determine the factors that might be influencing a member to have a shoppable procedure – past claims, prior authorizations, diagnostic attributes, member characteristics
  • Used a three-staged modeling approach to
    • Predict members who will have a specific shoppable service/claim
    • Predict members who tend to go towards high-cost providers
    • Predict members who will be responsive to an outreach program

Business Impact

  • Identified potential cost savings of USD 5 million for the client’s fully insured business
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