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Denials don’t just impact bottom lines; they clog workflows, delay reimbursements, and disrupt revenue cycles across hospitals and physician groups. For a top-tier Revenue Cycle Management (RCM) company serving large provider networks, the problem was growing rapidly.
They needed to go beyond conventional rule-based systems and introduce intelligence into the claims process, early enough to make a difference.
That’s when they partnered with CitiusTech.
The Challenge: Denial rates up, productivity down
Despite established RCM workflows, the volume and complexity of denials were rising. Identifying root causes across millions of claim lines required significant manual effort. Teams lacked real-time insight into which claims were most likely to be denied, and why.
It was time to make denial management proactive, not reactive.
The Solution: ML-powered denial prediction, purpose-built for Providers
CitiusTech deployed a machine learning-based denial prediction solution, leveraging rich historical claims data to assess denial risk and reduce manual review. The system analyzed over 10.5 million claim lines using advanced feature engineering, deep domain context, and proven ML techniques.
Exhibit 1: Importance value
Solution inputs & techniques
- Data Source: Claims from 835 remittance data, covering 10.5M claim lines
- Modeling: Classification & regression algorithms
- Feature engineering:
- Propensity features based on ICD, CPT, Revenue Codes, Payer Plan Code
- Embedding techniques like Word2Vec and semi-local embeddings to model CPT, ICD, and Revenue codes
- Semi-local features to capture intra-claim interactions
- Sampling techniques: Explored SMOTE, down-sampling, and others to address dataset imbalance
The model was designed not just for accuracy but for explainability, scalability, and seamless integration with RCM workflows.
The Results: Actionable intelligence. Real impact.
The ML-driven approach brought measurable improvement to denial prediction and RCM productivity:
- 95% model precision in identifying claims likely to be denied
- 74% recall, ensuring high sensitivity to at-risk claims
- 75% reduction in manual review effort, freeing up RCM staff for value-added tasks
- More accurate, faster insights into denial trends and contributing factors
- Improved first-pass claim success and lower denial-related rework
For the RCM company and its clients, the solution delivered a significant leap in operational efficiency and financial outcomes.
Building intelligent revenue cycles, claim by claim
Strong healthcare AI expertise, paired with scalable ML architectures, enabled the RCM partner to embed predictive intelligence at the point of claim processing. The result? Cleaner submissions, lower denial rates, and greater confidence in reimbursement outcomes.
