Workflow Map & Journey Overview
Understanding current state and planning future automation
Staff manually review claims for common errors before submission, leading to inconsistent quality and high touch time
Claims submitted with incomplete patient information, incorrect codes, or missing documentation
No standardized process for handling errors, leading to claims sitting in queues or being overlooked
Different teams and facilities follow different validation procedures, creating quality inconsistencies
15-20% denial rate erodes revenue and creates costly rework loops
Claims bounce back multiple times, increasing cost-to-collect
Staff perform the same validations repeatedly without automation
Quality depends on individual staff knowledge and attention to detail
Real-time checks against payer requirements before claim submission, catching errors early
Machine learning models identify high-risk claims and surface them for review before submission
Contextual suggestions help staff fix issues quickly with clear next steps and validation
Automated claim routing to appropriate queues and workflows based on risk level and issue type
Fewer claims requiring manual correction
More claims accepted on first submission
Reduced manual validation and correction time
Decreased operational expenses per claim