We Hooked up an AI Agent to an EHR. Here’s What Happened.

A few months ago, we decided to run an experiment.

We deployed one of Nanonets' AI agents into a live production EHR at a mid-sized multispecialty clinic in the Midwest.

The goal? See how much real-world admin work we could automate without human intervention.

Spoiler: It worked pretty well (not life-changing, but turned out to be quite useful).

In this post, we’ll walk through:

• What we plugged the agent into
• What it learned
• What it automated
• And what results we got within the first month

The Setup: One Clinic, One EHR, One Agent

EHR: athenahealth Daily volume: 80–100 new patient referrals, ~50 eligibility checks, 30+ prior auths Manual bottlenecks: Intake processing, auth follow-ups, fax uploads

We embedded a single Nanonets AI agent trained on:

• Patient intake forms (PDFs, faxes, emails)
• Eligibility APIs (via clearinghouse)
• Prior auth status portals (payer-specific)
• athenahealth's scheduling and chart modules

Week 1: Learning Mode

The agent started in "observe-only" mode. It:

• Ingested 1,200 documents from the past 90 days
• Watched how intake staff entered data
• Mapped key fields across sources (demographics, referring provider, reason for visit)
• Built confidence scores for OCR extraction

"We were surprised how quickly it adapted to messy, handwritten faxes. Even our staff makes mistakes there." — Practice Admin

Week 2: Small Tasks, Big Wins

We flipped the switch.

The agent began:

• Auto-parsing incoming referrals from fax/email inboxes
• Creating draft patient records in athena
• Tagging incomplete referrals for manual review

Impact:

• 73% of daily referrals were fully processed without human touch
• Staff saved 6–8 hours/day on intake alone

Week 3: Adding Eligibility & Auth Workflows

We extended the agent’s capabilities to:

• Pull real-time eligibility from payer APIs
• Store coverage summaries in athena's custom fields
• Initiate prior auth requests and track their status on payer portals

Highlight: The agent even flagged 3 patients with inactive Medicaid plans that would’ve slipped through otherwise.

"It’s like having a junior rev cycle analyst working 24/7. Except it doesn’t sleep or forget." — Rev Cycle Manager

Week 4: Scheduling Automation

Finally, we connected the scheduling module:

• The agent called eligible patients via AI voice assistant
• Confirmed DOB + referral reason
• Offered next available slots
• Booked appointments directly into athena

Stats:

• 57% of patients answered on first call
• 41% booked without staff intervention
• 89% satisfaction rate from follow-up SMS survey

So What Did We Actually Save?

Here’s a quick before/after snapshot after just 1 month:

Net productivity gain: ~27 staff hours saved per week

Unexpected Benefits

1. Referral-to-appointment time dropped by 2 days

• Faster intake + immediate scheduling = tighter loop

2. Fewer patient no-shows

• Automated confirmations + reminders increased show rate by 12%

3. Happier staff

• Intake staff shifted to exception handling and clinical coordination

Lessons Learned

• You don’t need a full data migration or EHR overhaul to use AI
• The agent learned by watching (no hardcoded rules)
• You get value even if it starts in "read-only" mode
• Fax-based practices benefit just as much as EHR-native ones

Would This Work In Your Clinic?

If your team is still manually:

• Copy-pasting referral data from emails
• Logging into payer sites for every auth
• Calling patients one-by-one to confirm appointments

...you're burning staff time and leaving revenue on the table.

Nanonets AI agents:

• Work with your EHR
• Learn your workflows
• Automate the boring parts

One agent. Four workflows. 27 hours back.

Want us to test one in your clinic? Request a pilot – see what the agent would automate in your setup.

Sources:

• Clinic pilot (Nanonets, 2024)
• MGMA RCM benchmark report
• athenahealth API usage logs (internal)