What was the impact of the AI solution?

Document processing time was reduced by more than 50%. Document processing costs were lowered by 40%. The solution improved accuracy by reducing human error in manual entries. Clinicians can navigate lengthy medical records and find evidence more efficiently.

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Source

Acentra Health: intelligent document processing for Medicare appeals and quality of care cases with Amazon Textract

Use case typeIntelligent document processingUpdated Oct 9, 2024

Acentra Health, a BFCC-QIO serving Medicare beneficiaries, built an intelligent document processing pipeline to handle appeals and quality-of-care cases more efficiently. The solution converts scanned images and faxed medical records into searchable text and stores extracted outputs and metadata for audit, analytics, and fast evidence access.

Organization: Acentra Health
Industry: Healthcare
Location: United States
Published: October 2024

Reported outcomes

−50%

document processing timeTime & speed

−40%document processing cost

Strategic outcomes

Cost efficiencyImproved clinician efficiencyCustomer experience & trustEnhanced patient experienceScale & capacityScaled to process 35 million pages a year

Catalog median for time & speed deployments: −60% across 727 reported metrics. Compare benchmarks →

Primary read

Use case focus

Showing 3 of 3

1Intelligent document processing
2Document processing automation
3Case management

Processing tens of millions of Medicare document pages each year with legacy OCR required manual search and data entry across multiple systems, creating delays, higher costs, and human error for clinicians reviewing appeal and evidence documents.

Acentra Health implemented a serverless intelligent document processing pipeline on AWS.
The workflow uses Amazon S3 for document storage, AWS Step Functions for orchestration, and Amazon Textract to extract text and key information from scanned images and faxed documents.
Extracted data and original documents are stored in Amazon S3, while metadata is kept for cost attribution, analytics, and audit purposes.
The solution also supports keyword-based bookmarking so healthcare practitioners can locate relevant evidence faster.

Technologies

Amazon Textract Amazon Simple Storage Service (Amazon S3)AWS Step Functions AWS Lambda

Document processing time was reduced by more than 50%.
Document processing costs were lowered by 40%.
The solution improved accuracy by reducing human error in manual entries.
Clinicians can navigate lengthy medical records and find evidence more efficiently.

Architecture

A serverless, event-driven IDP pipeline stores uploaded documents in Amazon S3, triggers AWS Step Functions workflows, sends files to Amazon Textract for OCR and extraction, and persists extracted data, originals, and metadata for audit and analytics. The solution also adds keyword-based bookmarking for faster evidence retrieval.

Sources & evidence1

Groundedness: 5/5Type: Blog PostPublished: Oct 9, 2024Publisher: AWSEvidence: VendorConfidence: Medium

Primary source

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Customer evidence