Prior authorization automation
Generating PA requests from clinical notes, tracking status, and surfacing the documentation each payer requires. Same day approvals where they were previously taking five.
Playbook
The healthcare AI playbook: HIPAA, prior auth, EHR integration, and what actually breaks.
A field tested playbook for AI work in a clinic, group practice, or specialty office. Five opportunities. Five risks. Three example workflows with real numbers. A 12 month action plan.
Why this matters
The state of AI in this vertical right now.
Healthcare AI is overhyped at the conference and underperforming in the office. The gap between vendor demos and clinic reality is wider than in any other vertical we work in. This playbook is the sober version. What actually pays back. What actually breaks. Where the compliance lines are. What to do this month, this quarter, this year.
Opportunities
Where AI is actually useful here.
Five places AI can pay back fast in a real practice. Not science fiction. Workflows that exist today and are deployable in weeks.
Denial appeal generation
Drafting peer to peer appeal narratives from denial reason codes plus the relevant clinical record. Consistency raises success rates 2x to 3x.
Clinical documentation
AI scribe tools that close visit notes within 24 hours. The gap between ambient capture and the structured note is the source of most documentation backlog.
Patient communication drafting
After visit instructions, appointment reminders, post procedure follow up. AI generates the first draft. A clinician reviews and approves before send.
EHR data mining for care gaps
Surfacing patients due for screenings, due for medication reviews, or showing risk patterns the structured EHR fields do not catch easily.
Risks
What to watch out for.
The places AI work in this vertical breaks. Read them before you sign a vendor contract or scope an internal project.
PHI exposure to vendors without a BAA
The single biggest mistake. Pasting PHI into a chat tool that has not signed a BAA is a HIPAA violation regardless of how careful you are.
Vendor BAA gaps with subprocessors
A vendor signs your BAA but their subprocessors did not. Get the subprocessor list in writing. Verify each one is covered.
Models trained on your patient data
Some vendors use customer data to improve their models by default. Get the opt out in writing before any patient data flows.
Audit log gaps
AI tools that do not log every PHI access at the user level fail your annual compliance review. Verify before you deploy.
Staff training that does not happen
Tools that do not get used produce nothing. Plan for 4 hours of training per staff member before launch and 1 hour quarterly thereafter.
Three example workflows
With concrete numbers.
Real workflows we have built or seen built. The numbers are conservative.
Workflow 01
8 to 12 hours/week saved
Prior auth automation for outpatient PT
A 4 provider PT clinic averages 25 prior auth requests per week. Each one takes 30 to 45 minutes manually. AI extracts clinical justification from the note, fills the payer specific PA template, and routes to the front desk for review and submission.
Time per PA drops to 5 minutes (with the human review). Time saved: 8 to 12 hours per week. Same day submission rate goes from 40% to 90%.
Workflow 02
Appeal success rate 2x to 3x
Denial appeal generation
A specialty practice receives 40 to 60 denials per month. Manual appeals get drafted by whoever has time. Quality is inconsistent. Success rate sits around 30%.
AI generates first draft appeals from the CARC code plus the relevant clinical record. A billing specialist reviews and submits. Consistency raises success rate to 60% to 80% depending on payer mix. Captures $20K to $50K per month in previously written off claims.
Workflow 03
30 to 45 minutes saved per visit
Clinical documentation closeout
An ambient AI scribe captures the visit. AI generates a structured SOAP note draft. The clinician reviews, edits, and signs within 24 hours instead of at week's end.
Time saved per visit: 30 to 45 minutes of after hours documentation. Note completion rate within 24 hours: 95% versus 60% manual. Provider burnout score measurably lower in 90 days.
What to do
This month. This quarter. This year.
30 days
This month
- 01Run the HIPAA readiness check for any AI tool currently in use.
- 02Inventory every vendor with PHI access. Confirm BAAs are signed and current.
- 03Pick one workflow from the opportunities list to pilot.
90 days
This quarter
- 01Scope the pilot using the AI pilot scoping worksheet (free download in the library).
- 02Run a 6 to 8 week pilot with success criteria written down up front.
- 03Document the compliance map in 01-architecture before any code or vendor contract.
12 months
This year
- 01Deploy the pilot to production with monitoring and a runbook.
- 02Run a tabletop incident response drill involving the AI tool.
- 03Annual review of every BAA, data flow diagram, and audit log retention.
- 04Pick the next workflow. The opportunities list does not get smaller as you tackle it.
How ByteWorthy works in this vertical
What an engagement looks like.
We run every engagement on the folder system. Discovery in 00. Architecture in 01. Build in 02. Deploy in 03. Operate in 04. Configuration locks in _config.
For this vertical specifically, the compliance scope is set in 01-architecture before any code is written. The vendor BAAs (or local infrastructure) get documented in _config. Every PHI flow, every privileged document, every audit log requirement is on paper before the build starts.
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