Company Profile
Company Type
Mid-size healthcare provider
Team Size
30-150 employees
Industry
Healthcare
Key Challenge
Struggling with inefficient manual data entry processes that were slowing growth and increasing operational costs. Their primary concern was HIPAA compliance.
Tools Connected
The Challenge
Data entry was consuming an enormous amount of this mid-size healthcare provider's time and budget. With 30-150 employees on staff, the healthcare organization was processing hundreds of documents, forms, and records daily — all manually. Two full-time data entry clerks spent their entire days keying information from various sources into their systems, and the team still couldn't keep up with the volume.
The error rate was the real problem. Manual data entry across their healthcare operations produced a 4.7% error rate — meaning roughly 1 in every 20 records contained mistakes. These errors cascaded through downstream processes, causing billing discrepancies, reporting inaccuracies, and customer-facing issues that damaged trust. The team spent an additional 15 hours per week just catching and correcting data entry mistakes. Meanwhile, critical healthcare records sat in processing queues for 3-5 business days, creating delays that rippled across the entire organization.
The Solution
The organization implemented Arahi AI to automate the entire healthcare data entry pipeline. They connected their document sources (Kareo, Epic, and file uploads) to Arahi AI's no-code platform and configured AI agents to handle extraction, validation, and system entry automatically.
The AI agents use OCR and natural language processing to read any incoming healthcare document — regardless of format — and extract structured data with 99.5%+ accuracy. Each extracted record passes through validation rules built specifically for their healthcare business: checking for completeness, format accuracy, logical consistency, and compliance with healthcare data standards. Valid records are automatically entered into Epic, while exceptions are flagged and routed to a human reviewer via Slack with specific error details and suggested corrections. The team went from processing 3-5 day backlogs to same-day data availability.
The Results
Measurable improvements across key healthcare data entry metrics.
Processing Time per Record
98% faster
Before
8-12 minutes
After
< 15 seconds
Error Rate
94% reduction
Before
4.7%
After
0.3%
Data Availability Lag
80% faster
Before
3-5 business days
After
Same day
Annual Labor Cost
95% savings
Before
$85K+ in staffing
After
$4K in AI processing
Processing Capacity
13x throughput
Before
150 records/day
After
2,000+ records/day
“What impressed me most was the setup speed. I expected a months-long implementation, but we had AI agents handling our healthcare data entry workflow within a single afternoon. The no-code approach meant our team could configure everything themselves without waiting on IT.”
Director of Business Operations
Mid-size healthcare provider
Key Takeaways
The most important lessons from this healthcare data entry automation project.
Automating data entry 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 data entry: 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 data entry 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
2 hours
AI Agents
3 AI agents
Tools Connected
5 integrations
Frequently Asked Questions
Common questions about automating data entry in healthcare.
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