Case Study

Cutting Claim Turnaround by 70% with Document AI and RPA

Claim turnaround time fell by 70%, with fewer manual errors and live visibility into the pipeline.

Industry

AI, Data & MLOps

Services We offer

AI / ML

Document Automation

RPA

Healthcare Integration

About the client

A national healthcare provider processing high volumes of claims.

Problem Statement

Claims were slowed by complex medical terminology and manual data entry that was both time-consuming and error-prone. The provider needed faster, more accurate processing without adding headcount.

The Challenges

Messy documents, clean data

Complex medical terminology and inconsistent inputs demanded document AI plus ML error resolution, not simple OCR.

Fits the existing stack

Integrating over FHIR and HL7 let automation slot into the provider's systems rather than replace them.

Approach

We combined document understanding, ML and automation:

AI-powered document understanding to read claims
Machine learning to detect and resolve errors
RPA to automate the manual, repetitive steps
Integration with healthcare systems over FHIR and HL7, plus real-time dashboards

The Solution

Documents are parsed with cloud vision and natural-language services, structured into a data warehouse, and pushed through automated workflows with RPA. Errors are flagged and resolved by ML rather than by hand.

The impact we delivered

1%

Faster claim turnaround

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Get Detailed Case Study

Cutting Claim Turnaround by 70% with Document AI and RPA

An automation layer that reads claim documents, resolves errors with machine learning, and integrates over FHIR and HL7, reducing turnaround time by 70%.

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