Company Profile
Company Type
Mid-size healthcare provider
Team Size
30-150 employees
Industry
Healthcare
Key Challenge
Struggling with inefficient manual invoice processing processes that were slowing growth and increasing operational costs. Their primary concern was HIPAA compliance.
Tools Connected
The Challenge
This mid-size healthcare provider had reached a breaking point with their manual invoice processing process. With 30-150 employees managing daily healthcare operations, the team was spending an average of 25+ hours per week on repetitive invoice processing tasks that added no strategic value. The workload was unsustainable, and errors were becoming more frequent as volume grew.
The consequences extended beyond wasted time. In their healthcare business, delayed invoice processing created a cascade of downstream problems — missed deadlines, frustrated stakeholders, and data quality issues that undermined decision-making. The team had tried hiring additional staff, but the cost was prohibitive and training new employees on their complex healthcare processes took months. They needed a solution that could handle their current volume and scale with their growth, without requiring a proportional increase in headcount.
The Solution
The team selected Arahi AI to automate their healthcare invoice processing workflow end-to-end. Implementation began with connecting their core tools — Epic, Kareo, and Slack — to the Arahi AI platform. Using the no-code builder, they configured AI agents that replicate their best-performing team member's decision-making process, but at machine speed and consistency.
The AI agents handle every step of the invoice processing process: receiving incoming requests or triggers, analyzing the context using healthcare-specific rules, making intelligent routing decisions, executing the core actions, and notifying the right stakeholders. What previously required 45+ minutes of manual work per instance now completes automatically in under 2 minutes. The agents also learn from corrections, continuously improving their accuracy. The team connected Google Forms for tracking and reporting, giving leadership real-time visibility into invoice processing performance metrics for the first time.
The Results
Measurable improvements across key healthcare invoice processing metrics.
Invoice Processing Time
Dramatically faster
Before
Days
After
Hours
Processing Cost per Invoice
Major savings
Before
High
After
Much lower
Error Rate
Major reduction
Before
Noticeable
After
Minimal
Early Payment Discounts Captured
Significant increase
Before
Rarely
After
Consistently
Monthly Invoice Volume
Major throughput increase
Before
Limited by capacity
After
Significantly higher
“Before Arahi AI, our invoice processing process was the bottleneck that every healthcare team complained about. Now it's our competitive advantage. We process faster, more accurately, and at a fraction of the cost. Our competitors are still doing this manually.”
Head of Strategy
Mid-size healthcare provider
Key Takeaways
The most important lessons from this healthcare invoice processing automation project.
Automating invoice processing in healthcare delivered immediate, measurable results: faster processing, higher accuracy, and lower costs.
The key to success was connecting existing healthcare tools to AI agents rather than replacing the entire tech stack.
24/7 automated processing eliminated backlogs and ensured consistent service quality regardless of volume fluctuations.
Starting with a pre-built template and customizing for healthcare-specific requirements dramatically reduced time-to-value.
Implementation Timeline
From zero to production in 2 hours — here's how they did it.
Step 1: Connected healthcare tools to Arahi AI
Integrated Epic, Cerner, and Athenahealth with Arahi AI using pre-built connectors — no API keys or custom code required. The team verified data flow between systems in under 15 minutes.
Step 2: Configured AI agent business rules
Defined the healthcare-specific rules for invoice processing: scoring criteria, routing logic, escalation thresholds, and exception handling. The team used Arahi AI's visual rule builder to translate their existing process into automated workflows.
Step 3: Tested with live healthcare data
Ran the AI agents on a week's worth of historical invoice processing data to validate accuracy and identify edge cases. Made minor adjustments to scoring weights and routing rules based on the results.
Step 4: Launched and monitored
Deployed the AI agents to production with the entire team notified via Google Forms. Monitored the first 48 hours closely, confirming high accuracy before reducing oversight to weekly reviews.
Setup Time
2 hours
AI Agents
3 AI agents
Tools Connected
5 integrations
Frequently Asked Questions
Common questions about automating invoice processing in healthcare.
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