Linking AI Initiatives to Real-World Outcomes

Use Case

CHARGEMASTER ANALYTICS: DETECT INCREASES IN BILLING AMOUNTS

 
Client

Large healthcare insurance provider based in Pennsylvania.

Problem

In healthcare insurance, a chargemaster is a comprehensive list of items billable to an insurance provider, usually containing highly inflated prices which is difficult to verify.

The client wanted to quantify and verify that quarterly increases in payments to providers was within permissible limits as dictated by contract language, CDM, cap and attestation information

Methodology
  • Combination of Deep Learning based professional service and modularized AI components used according to organization’s cognitive maturity
  • Business transformation with smarter cognitive outcomes
  • Adding industry, business and customer insights to the value chain
  • Seamlessly integrate data across functions
Solution
  • Advanced statistical and ML algorithms were used – both in the discrete and continuous domains – to detect billing amount increases with high confidence
  • Analyzed claims data, the increase in quarter-over-quarter billing is calculated for different revenue and CPT codes
  • For cost increases outside permissible limits, high confidence intervals are constructed to support results
  • Final outcomes from the negotiations have been incorporated as a feedback loop into the algorithms to improve accuracy
Benefits:
  • Over $50 Million in potential billing increases over permissible limits detected through deep insights on historical provider behaviour
  • Facilitated payer-provider contract negotiations
 

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