AI for healthcare that handles the paperwork
Custom AI for intake, prior authorization, clinical documentation, and claims, built on the systems you already own and deployed inside your HIPAA boundary.
How it works
Nobody trained for eleven years to retype insurance details off a faxed referral. Yet most health systems still run intake, authorization, and coding on scanned PDFs, payer portals, and phone queues that no EHR upgrade has ever fixed. We build custom AI that sits on top of what you already own, your EHR, your clearinghouse, your scheduling system, and absorbs the administrative work so your staff can stay with patients.
Intake and prior authorization, end to end
AI reads the referral, verifies coverage, assembles the payer packet, and chases the decision until it lands.
- Extracts data from faxed referrals, scanned IDs, and handwritten forms
- Runs real-time eligibility and benefits checks before the visit
- Builds payer-specific auth packets with the clinical evidence attached
- Tracks pending requests and escalates the ones going stale
Clinical documentation without the after-hours charting
Encounters become structured, codable notes in your EHR while the clinician is still in the room.
- Ambient capture across in-person, phone, and telehealth visits
- Specialty-tuned note templates, not generic transcript summaries
- Suggested ICD-10 and E/M codes with the supporting line cited
- Clinician reviews and signs every note, always the final word
PHI handling built to survive an audit
Every data path, model call, and access decision is designed for your compliance officer, not just for a demo.
- Runs in your VPC or a BAA-covered environment you control
- Minimum-necessary scoping and PHI redaction applied by default
- Immutable audit log of every inference and record touched
- SOC 2 controls, signed BAA, and no training on your patient data
Where teams use it
Built for real revenue work.
Multi-site groups buried in prior authorization
A single authorization can burn 30 minutes across three portals. AI assembles and submits the packet, then follows up, so coordinators work the exceptions instead of every case.
Revenue cycle teams fighting denials
AI checks coding, documentation, and payer rules before a claim leaves the building, and drafts the appeal with cited chart evidence when one still comes back.
Practices losing revenue to empty chairs
Models score each upcoming appointment for no-show risk using visit history, distance, and payer mix, then trigger the right outreach or backfill the slot from the waitlist.
“Our coordinators were spending more of the day in payer portals than with patients. The authorization workflow now runs itself for routine cases, and the team only touches what genuinely needs a human.”
FAQ
Questions, answered.
How is AI actually used in healthcare operations?
The highest-return uses are administrative rather than diagnostic: reading referrals and insurance cards, running eligibility checks, drafting prior authorization packets, generating clinical notes from the encounter, coding claims, and predicting no-shows. These are high-volume, rule-heavy tasks where AI removes hours of staff work per day. Diagnostic AI is a separate, heavily regulated category and is not what we build.
Is AI for healthcare HIPAA compliant?
It can be, but only if the architecture is built for it. We deploy inside your cloud environment or a BAA-covered tenancy, apply minimum-necessary scoping and PHI redaction before any model call, log every inference for audit, and contractually block your data from being used for model training. We sign a BAA and operate under SOC 2 controls. Compliance is a design decision made on day one, not a checkbox added at launch.
Will AI replace clinicians, coders, or front-desk staff?
No, and any vendor promising that is overselling. AI handles the repetitive extraction, drafting, and follow-up; people handle judgment, exceptions, and patients. In practice, coders review more charts per day and front-desk teams stop spending their mornings on hold with payers. Every clinical output is reviewed and signed by a licensed human before it counts.
How long does it take to deploy custom AI in a health system?
Most first workflows go live in 4 to 12 weeks, depending on integration depth and how many payers or sites are in scope. We scope a single high-volume workflow first, price it as a fixed project, prove the numbers, then expand. You own the resulting code and models outright, so nothing is locked behind a per-seat renewal.
Keep exploring
More from the Leadify platform.
Take the paperwork off your clinicians
Bring us one workflow and we'll come back with a scope, a fixed price, and a go-live date.