Agentic Prior Authorization IntelligenceAgentic Prior Authorization Intelligence

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.

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-auth agent to prepare review packages for clinicians. The agent extracted key details from both structured and unstructured medical records and created chronological clinical summaries that covered diagnoses, treatment history, notes, and patient details. It then retrieved relevant policy documents, summarized medical-necessity criteria, and checked patient records against authorization requirements.

The system let the agent complete the review-prep workflow autonomously and hand off a ready-to-review package to the clinician for final approval. Reviewers gained faster access to the clinical context they needed, while human-in-the-loop workflow ensured that decision authority remained with the clinical team.

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

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

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.