CitiusTech AI RCM Denial Prediction

The solution uses LLM-assisted pattern discovery, statistical analysis, healthcare coding hierarchies, and Knowledge Graph-based probabilistic matching to detect denial risks across payer, CPT/HCPCS, ICD-10, modifier, provider, place-of-service, authorization, CARC, and RARC dimensions.

It enables explainable pre-submission prediction, recommends corrective actions, and supports continuous learning from new claims, denials, and paid outcomes.

What Makes This Solution Different

• LLM-Assisted Discovery, Graph-Based Prediction: LLMs help interpret denial patterns, while predictions are generated through weighted Knowledge Graph matching, historical denial probability, and confidence validation.
• Claim Lifecycle Intelligence: Uses 837 submissions, 835 responses, denied claims, corrected resubmissions, and paid outcomes to identify real denial triggers.
• Delta-Based Root Cause Analysis: Compares denied claims with corrected resubmissions to identify changes in codes, modifiers, diagnosis sequencing, authorization, charges, frequency codes, and place of service.
• Validated Pattern Intelligence: Patterns are scored using denial frequency, paid frequency, CARC specificity, recency, coverage, trend behavior, and sample strength.
• Healthcare-Aware Generalization: CPT, ICD-10, and modifier hierarchies help generalize patterns beyond individual codes for broader reuse.
• Grounded and Explainable Outcomes: Predictions are based on validated patterns, graph relationships, lineage, and statistical confidence checks, reducing reliance on unconstrained generative inference.Key Solution Highlights
• AI-driven denial pattern discovery from claim submissions, denials, resubmissions, and adjudication outcomes.
• Azure Blob-based batch ingestion of 837 files with automated processing and audit logging.
• Unified analytical dataset across 837 claim submissions and 835 adjudication responses.
• Feature engineering across demographics, eligibility, coding, charging, payer, provider, authorization, and place of service.
• Knowledge Graph repository with pattern lineage, versioning, temporal tracking, and metadata traceability.
• Weighted pattern matching using exact, partial, semantic, and no-match logic.
• Denial probability calculation using matching score, pattern probability, Top-K similar patterns, and weighted aggregation.
• Built-in validation for similarity confidence, statistical strength, pattern stability, and conflicting pattern signals.
• Actionable recommendations to support pre-submission corrections and denial prevention.

Target

Users

• Executive Stakeholders: CFO,

CRO, VP Revenue Cycle, VP Revenue Integrity, Director Revenue Operations.

• Operational Stakeholders:

Revenue Cycle Managers, Denial Management Teams, Medical Coding Specialists,

Billing Teams, Claims Operations Managers, Revenue Integrity Analysts.

• Technology Stakeholders: CIO,

Chief Digital Officer, Data & Analytics Teams, AI Leaders, Enterprise

Architects.

Business

Outcomes

• Significant reduction in

false-positive denial alerts through probability-based pattern validation.

• Improved prediction precision

through deterministic Knowledge Graph pattern matching.

• No hallucination risk in

production denial prediction.

• Improved first-pass claim

acceptance rates and clean claim rates.