Streamlined claims prioritization for the largest PPO network provider in US

AI-powered claim priority score transforms negotiation outcomes and savings

A leading US PPO network offering cost management and data analytics solutions to Payers wanted to elevate their claims strategy through advanced AI/ML. With over 700 Payers and 40 years of historical claims data, the client sought to intelligently prioritize claims for negotiation.

CitiusTech developed sophisticated ML models that assigned a Claim Priority Score (CPS), helping negotiators target claims with the highest success potential and savings. The collaboration also enabled seamless adaptation to federal regulations, including the No Surprises Act (NSA).

The result? A cutting-edge NSA solution adopted by 100+ customers, managing 98,000+ Independent Dispute Resolution (IDR) requests in one year with a 62% success rate driving both compliance and competitive advantage.

The Challenge
Navigating high claim volumes with limited precision

Despite deep expertise, the client’s negotiators relied on intuition and manual effort to sort through vast claim volumes. This approach missed high-potential opportunities and left cost savings on the table. 

While they had begun exploring AI/ML, scaling these initiatives across model development, deployment, and maintenance proved challenging.

The complexity was further amplified by shifting healthcare dynamics and evolving regulations like the No Surprises Act (NSA), which introduced a labor-intensive dispute resolution process. They needed:

• A scalable model to prioritize claims based on historical success patterns
  
  
• Seamless integration into existing workflows
  
  
• A future-ready system with self-improving capabilities
  
  

The Solution
Scalable, smart, and self-improving ML engine

CitiusTech developed a machine learning–based engine that assigns a dynamic Claim Priority Score to each incoming claim. This score helps negotiators to prioritize claims on their worklist enabling fast, confident decision-making. The team focused on five key areas in model development and maintenance:

1. Model validation: This involves comparing the performance of a new model with the current one and deploying the new models only if it shows better performance.
   
   
2. Model deployment: The models were deployed into the production environment, enabling them to be actively used for making predictions.
   
   
3. Performance monitoring: Model performance is monitored by collecting data on the predictions made by the model and comparing these predictions to actual outcomes.
   
   
4. Defect triaging and resolution: Defect identification and triaging to assess the severity of the defect and determine the best course of action.
   
   
5. Continuous learning: If there is continuous decline in model performance as indicated by the key performance indicators (KPIs), the models undergo retraining or reengineering.

Cost-savings, lower manual effort, and stellar success rates

 The ML models transformed the negotiation process by intelligently ranking claims with a CPS, enabling negotiators to focus on high-impact opportunities. Seamless ML Ops practices ensured continuous model optimization and reliability. The IDR models significantly improved success rates for Payers winning IDR decisions by strategically targeting claims with the highest likelihood of savings.

This AI-led transformation enabled the client not only enhanced its internal operations but also positioned itself as a leader in innovative healthcare cost management solutions.