Authorization Workflow Orchestration

The orchestration engine evaluates the predictive authorization score assigned by a machine learning model to each request. Cases with a high probability of approval and complete clinical evidence are routed for straight-through processing, while low-confidence or high-denial-probability cases are automatically queued for specialized human review. This ensures that expert staff focus only on complex exceptions, maximizing operational efficiency.

The system distributes tasks across a heterogeneous workforce that may include internal clinical reviewers, outsourced RCM teams, and automated bots. It considers:

• Staff licensure and expertise: Matching complex cardiology cases to specialized nurses.
• Current queue depth: Balancing workloads to prevent bottlenecks.
• Service-level agreements: Prioritizing urgent or high-revenue requests to meet payer deadlines.

Unlike a simple stateless API call, the orchestrator maintains a persistent state machine for every authorization request. It tracks the exact stage—from clinical data abstraction and medical policy matching to payer portal submission and response parsing. If a human reviewer requests additional documentation, the workflow automatically pauses, sends a notification, and resumes precisely where it left off upon data arrival.

The orchestrator defines explicit paths for non-standard events. If a payer portal automation script fails due to a website change, the task is not dropped. Instead, it is automatically escalated to a manual processing queue with a detailed error log. Similarly, a peer-to-peer review request triggers a separate workflow that schedules the call and prepares a clinical summary for the physician.

A centralized dashboard provides a digital twin of the entire authorization operation. Managers can see:

• Authorization status tracking: Real-time counts of approved, denied, and pended requests.
• Bottleneck identification: Queues where average handle time is exceeding thresholds.
• Full auditability: A tamper-proof log of every automated decision, human action, and state change for compliance reporting.

The orchestration layer captures the outcome of every human review to refine the AI models. When a reviewer overturns an AI's medical necessity determination, that structured feedback is logged. This data is used to retrain the predictive authorization scoring model and update the payer rules engine, creating a continuous learning loop that progressively increases the auto-adjudication rate.

An AI-driven system that dynamically sorts pending authorization requests based on urgency, denial probability, or revenue impact to optimize clinical reviewer workflow.

• Routes high-probability approvals for straight-through processing
• Escalates high-risk cases to senior reviewers immediately
• Balances workload across available staff based on skill level
• Reduces average turnaround time by 40-60% in production deployments

The user experience design for clinical reviewers to efficiently audit and correct AI outputs based on model confidence thresholding.

• Presents pre-populated determinations for low-confidence cases
• Highlights specific evidence gaps requiring human judgment
• Captures reviewer corrections as feedback loops for model improvement
• Reduces cognitive load by surfacing only relevant policy criteria and clinical data

A machine learning model that assigns a probability score to a pending authorization request, predicting the likelihood of approval, denial, or the need for a peer-to-peer review.

• Scores generated in under 500 milliseconds at submission
• Enables intelligent routing decisions before human review begins
• Trained on historical payer adjudication patterns and clinical context
• Continuously updated as new determinations are recorded

A system that provides real-time visibility into the lifecycle of a prior authorization request, from submission and pendency to final payer adjudication and notification.

• Tracks state transitions: submitted, pended, approved, denied, appealed
• Generates audit trails for compliance and SLA monitoring
• Triggers automated notifications to providers and patients
• Integrates with EHR and practice management systems via FHIR APIs

A software-driven process where an AI system performs the initial clinical evaluation of an authorization request against medical policy, reserving human review only for complex exceptions.

• Applies payer-specific clinical criteria programmatically
• Auto-adjudicates up to 70% of routine requests without human touch
• Escalates cases where AI confidence falls below defined thresholds
• Maintains full documentation of automated decision rationale for audits

The automated process of comparing the clinical evidence provided in a request against the specific requirements of a payer's policy to identify missing or insufficient documentation.

• Flags missing lab results, imaging reports, or specialist notes
• Generates provider-facing checklists before initial submission
• Prevents avoidable denials by ensuring completeness upfront
• Reduces rework cycles and peer-to-peer review volume