Use case 1
Prior auth readiness
Pulls coverage, orders, and clinical evidence into a payer-ready packet, flags what is missing before submission, and leaves the submit step to your team.
For revenue cycle, patient access, and operations leaders
08
Healthcare operations AI that runs across your systems, inside your cloud.
OpenNash automates prior authorization prep, denial and appeal packets, referral intake, and payer follow-up across Epic, payer portals, and documents. Read-only pilots, audit logs, and human approval from day one.
01
The work that is quietly eating margin.
Revenue-cycle and intake admin, handled. PHI stays in your cloud.
1Prior authorization burns about 13 hours of staff time per physician every week, and roughly a third of requests still come back denied
2About 1 in 8 claims is denied on first pass, and 35 to 65 percent of denied claims never get reworked at all
3Registration and eligibility mistakes at check-in drive about a quarter of denials before a claim ever leaves the building
4Referrals stall between the referring office and the specialist scheduler, and roughly 38 percent never close the loop
5Faxes, payer letters, and outside records pile up in shared inboxes with no owner and no status
6Every AI pilot stalls in security review because nobody will let PHI leave the EHR or the approved cloud
02
The closed-loop system OpenNash builds.
Same operating pattern, tuned to Healthcare Ops workflows.
01 Intake
Capture
Read work from the tools your team already uses.
02 Reason
Decide
Classify, retrieve context, choose the workflow, and flag risk.
03 Draft
Produce
Create the response, report, memo, update, or work product.
04 Approve
Control
Route judgment-heavy or sensitive actions through human review.
05 Learn
Improve
Measure outcomes, exceptions, quality, and cycle time.
Feedback loop: every completed workflow becomes a signal for the next version.
03
High-value use cases.
Start where the volume, pain, and business value overlap.
Use case 2
Denial and appeal workbench
Reads the denial reason, assembles the appeal packet with payer and clinical evidence, and queues it by recovery value so high-dollar claims stop getting dropped.
Use case 3
Referral intake and chart prep
Reads inbound referral documents, builds the specialty prep checklist, and confirms receipt back to the referring office so referrals stop leaking.
Use case 4
Eligibility and registration check
Verifies coverage and catches demographic and benefit mismatches at intake, before they turn into front-end denials.
Use case 5
Document intelligence
Classifies faxes, outside records, and payer correspondence, extracts the missing-document signals, and ties each one back to the source record.
Use case 6
Payer follow-up worklist
Tracks open authorizations and claims, surfaces stale payer touches, and drafts the next follow-up with the linked context attached.
Note: OpenNash builds operational automation for revenue cycle and patient access. It does not do clinical decision support, diagnosis, or ambient scribing. Outputs are read-only and human-reviewed, with optional customer-approved writeback added later.
Related service pages:
AI agent implementation /
AI agent training
04
Trust, privacy, and source access.
How PHI, deployment, and source systems are handled.
PHI stays where your security team put it.
Most healthcare AI asks you to send PHI to a vendor and trust the contract. OpenNash is built to run inside your own cloud account, against the model service your security team already approved.
- Deploys into your AWS, Azure, or Google Cloud account, against your Epic FHIR credentials and approved document sources.
- Uses the model platform you already cleared: Azure OpenAI, AWS Bedrock, or Google Vertex AI, each HIPAA-eligible under your own BAA.
- Read-only first. Every output is source-linked and human-reviewed before any writeback happens.
- Audit logs and approval gates on every workflow, so compliance can see exactly what ran, on what record, and why.
Built for the systems your operations actually run on.
The pain rarely lives inside one system. It lives between the EHR, payer portals, billing tools, and the document inboxes where work goes to die.
- Epic, Oracle Health, athenahealth, and MEDITECH through read-only FHIR and approved integrations.
- Payer portals, clearinghouses, and eligibility services for status checks and follow-up.
- Faxes, PDFs, outside records, and payer letters parsed into structured, source-linked signals.
- Teams, Slack, ServiceNow, or your ticket queue, so exceptions land where staff already work.
05
Before and after.
Representative outcomes depend on scope, data quality, systems, and volume.
Before
Manual drag
- Staff spend the week on hold with payers and re-keying portals
- Denials are worked by hand, and most are never worked at all
- Referrals and faxes sit in shared inboxes with no owner
- Every AI idea dies in security review over PHI
to
After
AI-enabled ops
- Auth and appeal packets arrive built and sorted by dollar value
- Denials get queued and drafted instead of abandoned
- Every referral and document has an owner and a status
- Workflows run in your cloud, read-only first, with full audit logs
Auth prep time
-60%
Representative range
Denials left unworked
Near zero
Representative range
Referral leakage
-20-30%
Representative range
Front-end denials
-30-40%
Representative range
Next step
Pilot AI on your highest-volume revenue-cycle workflow.
Book a 30-minute meeting or email [email protected] with the workflow that hurts, the tools involved, and what success would look like in 90 days.