Agentic Prior Authorization Intelligence for Faster Clinical Decision-MakingAgentic Prior Authorization Intelligence for Faster Clinical Decision-Making

The client is a leading US-based health tech provider focused on simplifying administrative and clinical workflows across the healthcare sector. Their platform supports prior authorization reviews that involve medical records, policy documents, diagnosis and procedure codes, and authorization requests.

In traditional prior authorization workflows, Utilization Management reviewers have to work across fragmented records, policy documents, and clinical data before making a decision.

Even with a rapid rise in prior authorization volume, the company’s review process remained largely manual. Hence, the need was not just a faster approach but a more reliable way to organize information, validate requests, and support physicians and reviewers at scale.

Key Challenges

Reviewers spent too much time on redundant manual tasks, which hindered operational efficiency and timely access to care for patients.

1. Medical records arrived in EMRs, scanned documents, PDFs, and handwritten notes, making manual extraction and organization slow and inconsistent.
2. Policy documents were long, detailed, and spread across sources, so reviewers had to manually locate the right diagnosis or procedure criteria during each review.
3. Reviewers had to reconstruct clinical narratives and map them to changing policy rules, which limited throughput and introduced variability across decisions.
4. Clinical data received through EDI transactions had to be matched against extracted records, and mismatches created follow-up work and added cycle time.

The client sought a partner who understood the operational complexity of prior authorization and could help build a faster, more reliable review model. Coditas brought the business context, AI-native delivery depth, and Multi-Agent Systems thinking needed to connect fragmented clinical intake, policy retrieval, and validation into one controlled process.

Coditas built a prior authorization agent that prepared medical necessity review packages for clinicians. The system extracted relevant information from structured and unstructured medical records, then organized diagnoses, treatment history, clinical notes, and patient details into a chronological summary.

The agent also retrieved relevant policy content, surfaced applicable medical necessity criteria, and validated the extracted clinical information against authorization requirements. Clinical data received via Electronic Data Interchange transactions were cross-checked against information extracted from supporting records to identify inconsistencies before handoff to the reviewer.

Quality control remained part of the workflow through automated validation and clinician review. The system prepared the review package and surfaced the supporting clinical and policy context, while the clinical team retained responsibility for evaluating the evidence and approving the final decision.

Technologies

LLMs, Semantic Search, Electronic Data Interchange (EDI) Integration, Clinical Data Extraction, Policy Summarization, Type-Safe Parsing, Automated Validation, Secure APIs, and Observability

Our solution reduced the manual effort involved in assembling clinical and policy information for prior authorization review and gave clinicians a more structured package for medical necessity assessment.

The engagement improved review speed, accuracy, productivity, and production readiness. Key outcomes include:

• 60-70% reduction in review time
• 40% decrease in processing errors
• 3x improvement in reviewer throughput
• Pilot moved to production in under 30 days
• Faster authorization cycles supporting quicker patient access to treatment

“What impressed us most was Coditas’ ability to turn AI from an idea into something operational inside the business. They brought clarity to the strategy, speed to execution, and a strong focus on measurable outcomes from day one.”

— CEO & Co-founder, US Health Tech Platform

Prior authorization continues to challenge healthcare organizations because the work depends on fragmented clinical records, evolving policy criteria, and careful human judgment. Coditas approached this as a workflow transformation opportunity, helping the client build a more scalable and reliable review model with human-in-the-loop AI that reduces manual effort, supports faster decisions, and preserves clinician oversight.