Predictive Authorization Scoring

A core function that analyzes historical claims, clinical data, and payer behavior to forecast the risk of a denial before submission. The model ingests structured and unstructured data—diagnosis codes, procedure codes, and extracted clinical evidence—to output a score between 0 and 1.

• Input features: Patient demographics, diagnosis history, requested CPT codes, payer ID, and clinical narrative embeddings
• Output: A calibrated probability score (e.g., 0.87 = 87% denial risk)
• Action: High-risk requests are routed for clinical review and evidence strengthening prior to submission

A specialized classification layer that predicts the likelihood a request will escalate to a peer-to-peer (P2P) review rather than receiving a direct approval or denial. This intermediate outcome is critical for resource planning, as P2P reviews consume significant physician time.

• Key predictors: Policy ambiguity score, specialty mismatch between ordering and reviewing physician, and historical P2P rates for the specific payer-procedure combination
• Operational value: Enables proactive scheduling of specialist availability and preparation of targeted clinical arguments

The scoring model outputs not just a prediction but a calibrated confidence interval, enabling intelligent workflow routing. Requests with high-confidence approvals are auto-adjudicated, while low-confidence predictions—regardless of direction—are flagged for human review.

• High-confidence approval (>95%): Straight-through processing, zero human touch
• Low-confidence zone (60-95%): Routed to clinical reviewers with AI-generated evidence summaries
• High-confidence denial (>95%): Flagged for immediate provider intervention and documentation enhancement

Every prediction is accompanied by SHAP (SHapley Additive exPlanations) or LIME values that decompose the score into contributing factors. This transparency is essential for clinical reviewer trust and for providers to understand exactly what evidence is missing.

• Example output: 'Denial probability 78% driven by: missing PT/INR lab values (42% contribution), policy exclusion for diagnosis code M25.511 (31% contribution), and payer historical strictness (27% contribution)'
• Compliance: Supports auditability requirements under payer medical policy and state regulations

Rather than a single monolithic model, production systems deploy per-payer fine-tuned variants that capture the idiosyncratic adjudication patterns of each health plan. A procedure that is routinely approved by Payer A may be systematically denied by Payer B for the same clinical context.

• Training data: Segmented by payer tax ID, with separate calibration curves for each
• Drift detection: Monitors for concept drift when a payer updates its medical policy, triggering model retraining
• Cold start: For new payers, a Bayesian prior is initialized from the aggregate model and updated as data accumulates

The predictive score is exposed via a REST API endpoint that can be called at multiple points in the clinical workflow: during scheduling, at order entry, and immediately prior to submission. This enables a 'scoring cascade' where the prediction is refined as more clinical data becomes available.

• Scheduling call: Initial score based on patient demographics and planned procedure
• Order entry call: Score updated with diagnosis codes and ordering physician specialty
• Pre-submission call: Final score incorporating extracted clinical evidence and attachment completeness
• Latency SLA: < 500ms at the 99th percentile to avoid workflow disruption

An NLP technique that compares extracted patient-specific clinical data against a payer's formal medical policy documents to identify if coverage criteria are met. This deterministic check provides a critical feature input for the predictive model.

• Parses complex policy logic into machine-readable rules
• Flags specific criteria that are unmet or ambiguous
• Generates a policy adherence score as a model feature

An AI-driven system that dynamically sorts pending authorization requests based on urgency, denial probability, or revenue impact. The Predictive Authorization Score is the primary sorting key, ensuring high-risk cases receive immediate attention.

• Routes high-probability denials to senior clinical reviewers
• Balances workload based on predicted review complexity
• Reduces overall turnaround time for critical cases

The process of using natural language processing to identify and pull relevant clinical data points from unstructured medical records. The completeness and accuracy of extracted evidence directly impacts the reliability of the predictive score.

• Extracts diagnosis codes, lab values, and prior treatments
• Identifies missing documentation gaps before scoring
• Provides the structured input features for the ML model

A machine learning model that forecasts the final determination of a prior authorization request based on historical payer behavior, clinical context, and policy adherence patterns. This is the operational deployment of the Predictive Authorization Score.

• Classifies outcomes as Approve, Deny, or Peer-to-Peer Review
• Continuously retrains on new adjudication data
• Provides confidence intervals alongside point predictions

A software-driven process where an AI system performs the initial clinical evaluation of an authorization request against medical policy. Requests scoring above a high-confidence approval threshold are auto-adjudicated, reserving human review only for complex exceptions.

• Uses the predictive score to triage cases
• Reduces manual review burden by up to 70%
• Ensures consistent application of medical policy