It’s time for payers to experience a better way to identify anomalous claims. The common process to identify anomalies is business-rules-driven, manual intensive, applied in a post-pay scenario and focuses mainly on known patterns, thus solving the problem partially.
Applying advanced analytics and looking for opportunities beyond overpaid dollars, such as better utilization management, plan design changes and network optimization, helps detect hundreds of unknown anomalies.
Payers can use the new analytical framework to drive up to $50M additional impact within the first year of operationalization — by increasingly focusing on unknown anomalies.
Get better results by:
- Applying predictive analytics and AI to better prioritize claims for SIU review
- Automating the entire anomaly identification process
- Creating a visual solution suite to help identify anomalies, track alerts, and measure the impact of interventions
- Delivering significant impact in both post-pay and pre-pay scenarios
The significant ROI in initial year/s enables payers to self-fund a suite of more advanced AI-driven scalable solutions to keep identifying and tracking anomalies, and improving recoveries from the flagged claims.