Intake summarization, prior auth drafts, insurance letters, scheduling follow-ups. Admin workflows only. HIPAA-aware architecture with BAAs in place. $3,999/mo retainer.
- Analyze
- Automate
- Monitor
monthly retainer
Who this is for
Clinic owner, healthcare-ops director, or healthtech product lead where admin tasks are consuming clinical time. Staff spend their days re-entering intake data, chasing prior authorizations, and drafting insurance letters by hand. The clinical work suffers for it.
The pain today
- Admin eats clinical time: intake forms, scheduling follow-ups, insurance letters
- Prior authorization drafts take 30 to 60 minutes each and often get kicked back
- Patient intake data arrives unstructured and has to be re-keyed into the EHR
- Staff fear HIPAA exposure so AI tools sit unused or get misapplied
- No-show rates stay high because follow-up reminders are sent manually or not at all
The outcome you get
- AI automations for healthcare admin on a $3,999/mo retainer
- HIPAA-aware architecture with signed BAAs on every PHI-touching tool
- Intake data extracted and structured for clean EHR handoff
- Prior auth and insurance letter drafts with mandatory human review
- Scheduling follow-ups and care reminders automated within HIPAA guardrails
Where AI safely helps in healthcare admin
AI automation for healthcare works on the admin layer, not the clinical one. That distinction matters more here than in any other industry.
Four areas deliver clear ROI without touching clinical judgment. Intake summarization: an LLM extracts structured data from patient intake forms or transcripts and flags it for clinician review, cutting the re-entry step out entirely. Prior authorization: AI pulls the relevant clinical data from your records, cross-references payer criteria, and drafts the submission. Staff review and submit. Insurance letters: prior auth, appeals, and medical necessity statements drafted from structured matter data, reviewed by a billing specialist before they go out. Scheduling follow-ups: reminder sequences, no-show outreach, and care reminders sent via BAA-covered channels.
In each case, AI removes the typing time. Humans keep clinical judgment. Done right, clinical staff typically recover 5 to 15 hours per week.
AI automations for healthcare admin on a $3,999/mo retainer
HIPAA-aware automation architecture
Every PHI-touching AI component runs on HIPAA-compliant infrastructure with a signed Business Associate Agreement. Without a BAA, you cannot legally run AI on protected health information.
For LLMs: Azure OpenAI has a straightforward BAA. AWS Bedrock hosts Claude and other models under a BAA. Anthropic enterprise tiers have HIPAA-aware terms. For the highest-sensitivity workflows, self-hosted open-source models on HIPAA-eligible AWS or GCP eliminate the third-party data-sharing question entirely.
Beyond the LLM, every tool in the stack needs coverage: vector databases (self-hosted Qdrant or Postgres pgvector on HIPAA-eligible infrastructure), workflow engines (Temporal, Inngest), email and SMS (Paubox, LuxSci, Twilio with BAA). PHI minimization is enforced in prompts so only the data the task actually needs reaches the model. Audit logging covers every AI interaction: user, patient ID, prompt, output, model, timestamp, downstream action. Logs are retained for six years. This is baseline, not optional.
2M+: Records processed.
Prior authorization and insurance letters
Prior authorization is one of the highest-leverage targets for healthcare AI automation. A typical prior auth submission involves pulling clinical data, matching it against payer-specific criteria, and drafting a medical necessity statement. That process can take a billing specialist 30 to 60 minutes per case, and payers kick back a significant share on the first pass.
What I build: an automation that reads the relevant clinical record fields, maps them to the specific payer's criteria, and outputs a structured prior auth draft. The billing specialist reviews it, edits if needed, and submits. The AI does not submit autonomously. Appeals letters follow the same pattern.
The result is faster turnaround, fewer first-pass rejections because the draft is built directly against the payer criteria, and less cognitive load on billing staff. They're editing rather than drafting from scratch.
EHR integration and intake data extraction
Patient intake data is a constant friction point. Forms come in as PDFs, faxes, or unstructured portal submissions. Someone re-enters the relevant fields into the EHR. That step is slow, error-prone, and adds no clinical value.
AI automation can extract structured data from unstructured intake documents: chief complaint, medication list, known allergies, insurance details. The output is a structured payload ready for EHR import or manual clinician review, not a raw transcript.
I build these extractions with explicit output schemas so the model produces fields your EHR expects, not a freeform summary. PHI handling follows the same BAA and minimization rules as every other component. For practices with HL7 FHIR endpoints, the extraction can feed directly into the EHR API. For practices without, a structured review-and-confirm step keeps a human in the loop before any data lands in the record.
Scheduling follow-ups and patient reminders
Scheduling automation is one of the fastest-payback use cases in healthcare admin. Automated reminder sequences reduce no-show rates measurably and require almost no marginal staff time once set up.
What I build in this category: post-visit summary emails, appointment reminders, care-plan check-ins, medication adherence nudges. These go out via BAA-covered channels (Paubox for email, Twilio with BAA for SMS). Staff review templates; the automation handles the sending and timing logic.
The automation does not replace the clinical relationship. A reminder to take a medication at the prescribed time is an operational message, not clinical advice. That line stays clear in both the prompt design and the staff training that comes with the build.
Case: Reevia and the challenge of unstructured healthcare data
I built a data integration for Reevia that processed 2M+ records across four systems into a single HubSpot source of truth for one of Brazil's largest veterinary networks. The challenge was the same one healthcare admin teams face constantly: data living in multiple places, arriving in inconsistent formats, with no unified view.
The pattern translates directly to healthcare admin AI. Intake data, EHR records, insurance correspondence, and billing data all live in separate systems. The automation work is about extracting, normalizing, and routing that data so the right person sees the right thing at the right time. Reevia went from zero cross-system visibility to a full operational picture in four weeks. The same structured-extraction approach applies to healthcare admin workflows: defined schemas, explicit handoff points, and a human review step before anything writes to a system of record.
When specialist clinical-AI vendors are the right call
For clinical decision support, diagnosis assistance, treatment recommendations, or risk scoring, use FDA-cleared vendors: Aidoc, Viz.ai, Nuance DAX Copilot for ambient documentation, Suki for AI-assisted notes. My retainer covers admin AI, where humans retain clinical judgment and AI handles the typing.
Many healthcare customers run both: a clinical AI platform for clinical workflows plus my retainer for the admin layer those platforms do not cover. They are complementary. I help you think through which category your workflow falls into before you commit to either. If it belongs to a specialist clinical vendor, I'll tell you that directly rather than take the work.
Recent proof
A comparable engagement, delivered and documented.
Four systems, one source of truth: HubSpot visibility for one of Brazil's largest vet networks
Built a custom integration layer for Reevia that connects four source systems into HubSpot for one of Brazil's largest veterinary companies. Over 2 million records processed with full normalization. Any lead from any system is inside HubSpot in under 50 seconds, standardized and ready to use.
Read the case studyFrequently asked questions
The questions prospects ask before they book.
Azure OpenAI has a straightforward BAA. AWS Bedrock (which hosts Claude, Llama, and other models) has a BAA. Anthropic enterprise tiers have HIPAA-aware terms. OpenAI enterprise requires negotiation to get a BAA. For most healthcare admin workloads, Azure OpenAI or AWS Bedrock are the default because the agreement process is well-established. Self-hosted open-source models on HIPAA-eligible AWS or GCP infrastructure are available for highest-sensitivity workflows where third-party data processing is off the table.
AI can draft prior authorization submissions, not submit them autonomously. The workflow: AI reads the relevant clinical data, maps it against the specific payer's medical necessity criteria, and produces a structured draft. A billing specialist reviews, edits, and submits. This removes the manual research and drafting time, which is where the hours go. The human stays in the loop before anything goes to the payer. For appeals letters, the pattern is the same.
Integration depth depends on your EHR. For systems with HL7 FHIR APIs (Epic, Athenahealth, Cerner), structured extraction outputs can feed directly into the EHR via the API. For systems without modern API access, the integration is a structured review-and-confirm step: AI extracts and formats the data, a staff member confirms, then the data is manually entered or imported. Either way, no AI writes directly to the EHR without a human review step in the workflow.
Every AI interaction should log: user identity, patient ID when applicable, the prompt sent (with PHI noted), the model output, which model was used, timestamp, and what downstream action was taken. Logs are stored in HIPAA-compliant infrastructure and retained for six years to satisfy HIPAA's documentation requirements. Queryable logs are important for compliance reviews and breach investigation. AI that cannot be audited creates regulatory exposure, so this is built in from the start.
Under HIPAA's TPO (Treatment, Payment, Operations) provisions, patients do not need separate consent for admin-side AI that processes their data for operational purposes. The practice's Notice of Privacy Practices should disclose that AI tools support administrative work. For AI-generated patient-facing content like reminders or care summaries, consent is typically covered by the general treatment consent. Your compliance team owns the consent language; I build the technical implementation to match it.
Admin AI extracts, summarizes, drafts, schedules, and routes. Clinical AI diagnoses, recommends treatment, scores risk, or interprets test results. The line matters because clinical AI carries FDA regulatory considerations and liability that admin AI does not. Nuance DAX Copilot and Suki are clinical documentation tools built specifically for that regulatory context. My retainer is for the admin side: insurance letters, intake extraction, reminders, scheduling follow-ups. Many clinics run both layers side by side.