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CODING
Charge Capture and Coding for RCM Automation
Nanonets Health AI Coder automates charge capture and coding from completed clinical documentation, converting documented care into accurate, validated, billing-ready charges. The demo shows how clinical notes are translated into CPTs, modifiers, and diagnoses using facility-specific rules, with every coding decision clearly explained and validated before moving downstream.
What this solves:
- Eliminates manual charge capture and code selection
- Reduces coding variability across providers and facilities
- Ensures coding decisions are defensible and audit-ready
- Prevents missed or incorrect charges caused by documentation gaps
- Accelerates claims readiness without adding coding headcount
This is core RCM automation built for real coding workflows, where accuracy, consistency, and revenue integrity matter.
Doctor-Built AI: Because Time is Money, and you're losing both.
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Patient Intake
Nanonets Health Payment Posting and AR AI Agent automatically ingests EOBs, extracts line-item payment data, and reconciles it against charges in the EHR. This demo shows how payments, contractual write offs, and patient responsibility are identified from the EOB, validated, and posted accurately, with updates reflected in both Nanonets Health and the EHR.
What this solves:
- Eliminates manual EOB review and payment posting
- Ensures accurate application of payments, adjustments, and patient responsibility
- Reduces posting errors and reconciliation gaps
- Keeps AR in sync without manual follow up
- Improves payment visibility and downstream collections
This is downstream RCM automation built for real payment posting workflows, where accuracy, traceability, and financial integrity matter.
Eligibility & Benefits
Nanonets Health Eligibility and Benefits Agent automates insurance verification directly inside the EHR, ensuring coverage, benefits, and patient responsibility are confirmed before the visit. This demo shows how insurance ID cards trigger automated eligibility checks, where the agent identifies the payer, verifies coverage dates, determines primary and secondary insurance, and retrieves benefit details such as copays, deductibles, and out of pocket limits. Verified information is written back into the patient’s insurance record, with no manual follow ups.
What this solves:
- Eliminates manual eligibility and benefits verification work
- Reduces front desk errors caused by outdated coverage
- Ensures primary and secondary insurance are identified correctly
- Prevents downstream denials due to eligibility issues
- Keeps insurance data accurate across intake, scheduling, and day of service
This is upstream RCM automation built for real eligibility workflows, where coverage accuracy and timing directly impact collections and patient experience.
Voice agents
Nanonets Health Scheduling AI Agent schedules patient appointments through live voice conversations. The agent verifies patient identity, discusses availability, answers practical questions, and confirms appointments, with all updates synced directly to the EHR. This demo includes a live voice call with the scheduling agent in action.
What this solves:
- Eliminates manual outbound and inbound scheduling work
- Reduces missed calls and delayed bookings
- Handles patient questions without staff involvement
- Ensures appointments are accurately synced to the EHR
- Gives clear visibility into scheduled and pending cases
This is downstream RCM automation built for real scheduling workflows, where speed, accuracy, and patient experience matter.
Patient Intake
Nanonets Health Patient Intake AI Agent automatically processes referral packets from fax and email, creating clean, EHR-ready patient records.
What this solves:
- Eliminates manual referral sorting and data entry
- Handles multi-patient packets without human intervention
- Captures consistent demographic, insurance, and clinical data
- Reduces intake errors that lead to downstream denials
- Speeds up intake without adding headcount
This is upstream RCM automation built for real intake workflows, where faxes are messy, referrals are incomplete, and accuracy matters.
Agentic workflows
The era of agentic AI in healthcare RCM has arrived. As a specialist provider, if you're still processing referrals by manually reading faxes and combing through 100-page patient summaries, you're doing it wrong.
In this demo, our product lead explains how Nanonets Health AI agents can:
- Read complex patient documents
- Do patient intake into your EHR
- Verify insurance across 150+ payers
- And then call up the patient to schedule an appointment!
Consult with our Automation Team today!
