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Bill Review Automation



The client is a leading healthcare payment company with over 1500 employees. They had an acute need to streamline their claims process for financial optimization.


  • Automatic processing of healthcare claims submitted in non-standard formats
  • Aim was to replace existing 3rd party OCR Bill review system with a new product owned by the client.
  • MVP Scope: Train the product on 800+ unique claim formats


The MVP included features to upload documents, review & edit the extraction, assign denial reasons to individual line items, export the output, summary view by Rev Code and user access management.

Technology Stack

  • Azure Forms Analyzer
  • Azure Functions
  • Azure Web APIs
  • ReactJS Web Application

Key Achievements

  • Team Size: 8 (1 PM/BA, 4 Developers, 3 QA)
  • Timeline: Ideation to MVP in 9 months
  • Metrics: Achieved 85% accuracy at a field-level
  • High accuracy with challenging data objects such as tables

Quality Assurance: Restructuring and Enabling Automation ​


Client Overview

A third-party administrator who supported payors with adjudicating out of network claims. The process involved time-sensitive and number-centric scenarios with complex test conditions and heavy test data dependency. The client’s key bottleneck was to edit solution functionality.




  • Insufficient test coverage and test cycle time to conduct the edit code testing led to high functional defect leakages.  
  • Extensive claims test data is needed as a pre-requisite to validate the Edits during every release cycle.  
  • Test coverage and test data issues led to quality issues which directly impacted the revenue and dissatisfied customer experience.


Genzeon took over the QA function from the in-house team and assessed the current QA. We defined a 30-60-90-day plan with the following focus:  

  • Prepared the requirement traceability to determine coverage gaps 
  • Built test cases to improve the overall test coverage 
  • Initiated test automation to build the automated test coverage  
  • Reduced the test data generation and cleanup process
  • The 14-person onshore QA team was transitioned to an onshore/offshore model 


Metrics improved significantly. 

Total Test Cases: 800  Total Test Cases: 1,700 
Automated Test Cases: 0  Automated Test Cases: 1,350 
Defective Leakage: 8%  Defective Leakage: 1% 
Automation Coverage: 0%  Automation Coverage: 80% 
Test Data Effort: 16 hours each cycle  Test Data Effort: 6 hours each cycle 

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