How a Specialty healthcare practice Automated Appointment Scheduling with Arahi AI
See how a specialty healthcare practice automated appointment scheduling with Arahi AI. Results: 97% faster task completion time, 250% increase team productivity. Read the full case study.
Company Profile
Company Type
Specialty healthcare practice
Team Size
100-500 employees
Industry
Healthcare
Key Challenge
Struggling with inefficient manual appointment scheduling processes that were slowing growth and increasing operational costs. Their primary concern was regulatory requirements.
Tools Connected
The Challenge
Manual appointment scheduling was the biggest bottleneck in this specialty healthcare practice's operations. Their team of 100-500 employees processed hundreds of appointment scheduling requests weekly, each requiring multiple steps, cross-referencing against healthcare-specific requirements, and coordination between departments. The average appointment scheduling request took 45 minutes to complete manually, and the backlog was growing by 15% each quarter.
Beyond the time drain, the quality of their appointment scheduling output was inconsistent. Different team members followed different procedures, and there was no standardized way to handle edge cases that are common in healthcare. A recent audit revealed that 12% of completed appointment scheduling records contained errors that required rework — costing the organization an additional $50K annually in correction and remediation efforts. The leadership team recognized that continuing to throw people at the problem wasn't viable and began searching for an AI-powered solution.
The Solution
Arahi AI provided the automation backbone this healthcare team needed. They deployed a multi-agent workflow that breaks the appointment scheduling process into discrete, automated steps — each handled by a specialized AI agent. The first agent monitors triggers from Epic and Kareo. The second agent analyzes and processes incoming requests using healthcare-specific business logic. The third agent executes actions across connected tools and notifies team members via Slack.
The beauty of the no-code approach was speed of implementation. The team had their first agent live within 90 minutes, and the full appointment scheduling workflow was operational within a single afternoon. They used Arahi AI's template for healthcare appointment scheduling as a starting point, customized the business rules to match their specific process, and connected their existing tool stack without writing a single line of code. Within the first week, the agents had processed over 200 appointment scheduling instances with 99%+ accuracy — more than the team typically handled in a month.
The Results
Measurable improvements across key healthcare appointment scheduling metrics.
Task Completion Time
97% faster
Before
2-3 hours average
After
< 5 minutes
Team Productivity
250% increase
Before
Baseline
After
3.5x output
Quality Score
26% improvement
Before
78% accuracy
After
98.5% accuracy
Monthly Cost
85% savings
Before
$8,200/month
After
$1,200/month
Customer Satisfaction
35% increase
Before
3.4/5
After
4.6/5
“The difference is night and day. Our healthcare clients used to wait days for appointment scheduling to be completed. Now it happens in minutes, and the quality is consistently higher than what we achieved manually. Customer satisfaction scores went through the roof.”
VP of Customer Success
Specialty healthcare practice
Key Takeaways
The most important lessons from this healthcare appointment scheduling automation project.
AI-powered appointment scheduling automation eliminated 88% of manual processing time for this healthcare team, freeing staff to focus on high-value strategic work.
Implementation took less than a day — the no-code approach meant no IT bottleneck or months-long development cycle.
Error rates dropped by over 90%, significantly improving data quality and downstream decision-making.
The ROI was realized within the first month, with the solution paying for itself multiple times over through cost savings and productivity gains.
Implementation Timeline
From zero to production in Half a day — here's how they did it.
Step 1: Mapped the existing appointment scheduling workflow
Documented every step of the current manual appointment scheduling process, including decision points, exceptions, and handoffs between team members. Identified which steps could be fully automated versus those needing human oversight.
Step 2: Built the automation in Arahi AI
Used Arahi AI's no-code builder to create the appointment scheduling workflow: connected Epic and Athenahealth as data sources, configured AI decision logic for healthcare-specific requirements, and set up automated actions and notifications.
Step 3: Parallel run with manual process
Ran the AI agents alongside the manual process for one week to compare outputs. The AI matched or exceeded human accuracy on 98% of appointment scheduling instances, with the 2% of edge cases automatically flagged for human review.
Setup Time
Half a day
AI Agents
2 AI agents
Tools Connected
5 integrations
Frequently Asked Questions
Common questions about automating appointment scheduling in healthcare.
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This case study represents a typical customer scenario. Individual results may vary.