Company Profile
Company Type
Multi-location health clinic
Team Size
50-200 employees
Industry
Healthcare
Key Challenge
Struggling with inefficient manual report generation processes that were slowing growth and increasing operational costs. Their primary concern was patient data security.
Tools Connected
The Challenge
This multi-location health clinic had reached a breaking point with their manual report generation process. With 50-200 employees managing daily healthcare operations, the team was spending an average of 25+ hours per week on repetitive report generation 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 report generation 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 report generation 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 report generation 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 report generation performance metrics for the first time.
The Results
Measurable improvements across key healthcare report generation metrics.
Processing Time
96% faster
Before
45+ minutes per task
After
< 2 minutes
Manual Hours per Week
88% reduction
Before
25+ hours
After
< 3 hours
Error Rate
92% fewer errors
Before
8-12% manual errors
After
< 1% with AI
Operational Cost
88% savings
Before
$6,500/month
After
$800/month
Team Capacity
10x scale
Before
Limited by headcount
After
10x throughput
“We went from spending half our day on report generation to having it just happen automatically. The AI agents handle the routine work perfectly, and our healthcare team can focus on the strategic decisions that actually move the needle. I wish we had done this a year ago.”
VP of Operations
Multi-location health clinic
Key Takeaways
The most important lessons from this healthcare report generation automation project.
This healthcare team proved that report generation automation doesn't require technical expertise — the no-code platform made it accessible to business users.
Scaling report generation capacity by 10x without adding headcount fundamentally changed the economics of their healthcare operations.
Consistent AI-powered processing eliminated the quality variance that came with different team members handling report generation differently.
Real-time visibility into report generation metrics gave leadership the data they needed to make better strategic decisions.
Implementation Timeline
From zero to production in 3 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 report generation: 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 report generation 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 99%+ accuracy before reducing oversight to weekly reviews.
Setup Time
3 hours
AI Agents
4 AI agents
Tools Connected
5 integrations
Frequently Asked Questions
Common questions about automating report generation in healthcare.
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