Company Profile
Company Type
Mid-size healthcare provider
Team Size
50-200 employees
Industry
Healthcare
Key Challenge
Struggling with inefficient manual follow-up processes that were slowing growth and increasing operational costs. Their primary concern was patient data security.
Tools Connected
The Challenge
This mid-size healthcare provider had reached a breaking point with their manual follow-up process. With 50-200 employees managing daily healthcare operations, the team was spending an average of 25+ hours per week on repetitive follow-up 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 follow-up 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 follow-up 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 follow-up 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 follow-up performance metrics for the first time.
The Results
Measurable improvements across key healthcare follow-up metrics.
Processing Time
Dramatically faster
Before
Lengthy manual process
After
Minutes
Manual Hours per Week
Major reduction
Before
Many hours
After
Minimal oversight
Error Rate
Significantly fewer errors
Before
Noticeable manual errors
After
Minimal with AI
Operational Cost
Major savings
Before
High
After
Much lower
Team Capacity
Significant scale
Before
Limited by headcount
After
Dramatically higher throughput
“The ROI came quickly. Our follow-up throughput increased significantly while our error rate dropped dramatically. For a healthcare business of our size, that translates directly to the bottom line.”
Operations Director
Mid-size healthcare provider
Key Takeaways
The most important lessons from this healthcare follow-up automation project.
This healthcare team proved that follow-up automation doesn't require technical expertise — the no-code platform made it accessible to business users.
Scaling follow-up capacity dramatically 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 follow-up differently.
Real-time visibility into follow-up metrics gave leadership the data they needed to make better strategic decisions.
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 follow-up: 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 follow-up 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
4 AI agents
Tools Connected
5 integrations
Frequently Asked Questions
Common questions about automating follow-up in healthcare.
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This case study represents a typical customer scenario. Individual results may vary.