Prior authorization processes relied on data from multiple stakeholders, often resulting in missing or incorrect information. These inconsistencies delayed approvals and slowed down care delivery.
Reviewing and validating authorization documents required significant manual effort. This process increased errors and extended turnaround times for approvals.
Used OCR and large language models to extract and structure key data from unstructured documents.
Developed a configurable rule engine to assess prior authorization data against specific medical and payer requirements.
Transitioned from a monolithic system to microservices to improve flexibility, throughput, and performance.
Built unit, integration, and system testing to sustain system dependability across updates.
Generated structured PDF reports with clear validation outcomes to simplify review processes.
Accelerated prior authorization processing by 3X, reducing delays and improving turnaround time for critical approvals.
Reduced manual effort and errors 70% through automated data extraction and validation workflows.
Sustained 95%+ data extraction accuracy across growing document volumes, supporting consistent performance as caseloads expanded.
