Client Success
Reducing manual review by 60% across a healthcare revenue-cycle operation
Indus Health, an organization managing revenue-cycle and medical billing operations in healthcare.
Reduced the volume of cases requiring manual review by 60%, while maintaining 95% accuracy across 250 evaluated cases.
Challenge
The operational challenge
A high volume of billing and claims cases required manual review before they could move forward, consuming reviewer time and creating a bottleneck between incoming case volume and available review capacity.
Approach
How we approached it
We designed a multi-agent AI revenue-cycle platform modeled on how a real RCM team works: eight specialized agents handle documentation, coding, validation, payer-policy research, compliance, and denial analysis and appeals, coordinated by LangGraph with explicit feedback loops, quality gates, and human-escalation paths built into the workflow from the start.
Outcome
What changed
The system reduced the volume of cases requiring manual review by 60%, while maintaining 95% accuracy across 250 evaluated cases — with every case still passing through human-review gates rather than being resolved without oversight.
“The most impressive part was how the AI was designed around the existing revenue-cycle workflow rather than trying to automate everything in one step. The human-review gates and policy-grounded decisions made the approach feel much more realistic for healthcare.”
Rizwan Ahmad, Revenue Cycle Stakeholder
Governance
What stayed human
Every case still moves through explicit quality gates and human-escalation paths built into the workflow — specialized agents support documentation, coding, payer-policy research, and denial analysis, but the cases that need human judgment are routed to a person by design. The system was built around the existing revenue-cycle process rather than replacing it in one step, so accountability for a claim decision never moves off a person's desk.
Lessons
What we learned
Designing the human-review gates and escalation paths as part of the system from day one, rather than adding oversight after the fact, was what made the approach credible inside a real revenue-cycle team, not just technically functional.
Services
Capabilities used in this engagement
Intelligent Business Workflows
Improve operational efficiency through workflow automation and decision support.
- Reduced manual work
- Faster approvals
- Process automation
AI Governance & Evaluation
Give every AI system a named owner, a documented escalation path, and a way to prove it's still working as intended.
- Evaluation scorecards
- Human escalation design
- Audit trails
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