prior_authorization · healthcare · workflow

Myriad Genetics achieves 77% cost reduction and 80% faster document classification with AWS GenAI IDP Accelerator

Myriad Genetics processed thousands of healthcare documents daily across Women's Health, Oncology, and Mental Health divisions, but their existing Amazon Textract and Amazon Comprehend pipeline cost $15,000 per business unit monthly at 3 cents per page and took 8.5 minutes per document to classify. Information extraction remained entirely manual, requiring up to 10 full-time employees contributing 78 hours daily in the Women's Health unit alone.

How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Order event triggers ingestion
Incoming order events trigger document retrieval from source document management systems.
Tools used
Amazon BedrockAmazon Nova ProAmazon Nova PremierAmazon TextractAmazon ComprehendGenAI IDP AcceleratorAnthropic Claude Sonnet 3.7Amazon Nova Lite
Outcome

The new solution increased classification accuracy from 94% to 98%, reduced classification costs by 77% (from 3.1 to 0.7 cents per page), and cut classification time by 80% (from 8.5 to 1.5 minutes per document). Automated KIE achieved 90% accuracy matching the manual baseline. Myriad will realize up to $132K in annual savings, and the solution saves 300 hours monthly across Women's Health prior authorizations, reducing each prior authorization submission time by 2 minutes.

What failed first

Despite 94% classification accuracy, the existing Amazon Textract and Amazon Comprehend solution suffered from high operational cost and latency, and misclassified documents due to structural similarities and overlapping content across document types. Information extraction could not be automated because it required contextual understanding to differentiate clinical distinctions and locate information across varying document formats.

Results
Time saved$15,000 monthly
Volume94% to 98%
Cost replaced77%
Source

https://aws.amazon.com/blogs/machine-learning/how-myriad-genetics-achieved-fast-accurate-and-cost-efficient-document-processing-using-the-aws-open-source-generative-ai-intelligent-document-processing-accelerator?tag=soumet-20

How we source this →

Grounding & classification
Source type: platform led case
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