End-to-End Prior Authorization Intelligence with GenAI

A healthcare technology provider focused on simplifying administrative and clinical workflows across the healthcare ecosystem. Their platform supports large-scale prior authorization programs where teams review extensive medical records and navigate complex policy documentation.

Even with a rapid rise in the volume of prior authorization requests, the review process remained heavily manual for the client’s team. Utilization Management reviewers and support teams spent too much time piecing together clinical context, searching policy criteria, and validating incoming request data. This hindered operational efficiency and timely access to care for patients.

High Volume of Unstructured Records

Medical records arrived in multiple formats, including EMRs, scanned documents, PDFs, and handwritten notes. Manual extraction and organization of clinical facts resulted in inconsistent intake and increased labor intensity.

Error-Prone Policy Navigation

Policy documents were long, detailed, and spread across sources. Reviewers had to manually locate the appropriate diagnosis or procedure-specific criteria, which slowed decision-making and increased the risk of oversight.

Inconsistent Review Cycles

After pulling the clinical facts, reviewers still had to stitch patient history into a clear narrative and map it to changing policy rules. The work relied heavily on individual judgment, which limited throughput and created variability across decisions.

Data-Validation Complexity in EDI Workflows

Clinical data received via EDI transactions had to match the data present in the extracted records. Any mismatch triggered follow-up and rework, disrupting downstream workflows and adding avoidable cycle time.

Coditas designed and implemented a GenAI-powered decision support copilot tailored to prior authorization workflows. The intent was to unify clinical context, policy guidance, and request validation in a single, workflow-ready experience.

Structured Clinical Context

We enabled rapid extraction and organization of key clinical information from mixed record formats, so reviewers could start with a clear, consistent clinical narrative rather than having to reconstruct it.

Policy-Aware Guidance

We made policy criteria easier to apply during review by surfacing the most relevant requirements in context, helping teams move faster without losing rigor.

Review Standardization Support

A workflow support pointed reviewers to the right evidence and rationale, reducing variability across reviews while keeping the final judgment with the reviewer.

EDI-Ready Validation and Interoperability

Request intake was aligned with EDI validation workflows to reduce mismatches, limit rework, and improve downstream reliability.

Production Readiness

Our team supported rollout with monitoring, controls, and governance-focused safeguards suited for high-volume operations.

The GenAI copilot improved speed, accuracy, and reviewer capacity across the client’s prior authorization operations.

• 60 to 70% reduction in review time through automated extraction and targeted policy retrieval.
• 40% decrease in processing errors by validating EDI fields against structured summaries.
• 3x more reviewer throughput by reducing repetitive navigation and surfacing evidence faster.
• <30 days from pilot to production thanks to a modular, API-first architecture.
• Faster patient access to treatment through reduced approval delays and improved care continuity.

This engagement showed what changes when GenAI is applied with the right guardrails and workflow integration. Coditas helped the client move from manual, fragmented review cycles to a structured, evidence-led process that reviewers can trust and scale.

You too can modernize your company’s prior authorization program without weakening governance.

Reach out to our experts to discuss a tailored solution.