Custom AI Agents in Healthcare for Scheduling, Intake and Prior Authorization
We build AI agents that take over the scheduling, intake, referral and prior authorization work your staff does by hand, inside the EHR and practice systems you already run. A clinician signs off on anything clinical, and patient data stays in infrastructure you control.
Only the fields this task needs. The rest of the chart is never retrieved.
Patient nameJ. D••••
Reason for referralVisible
Payer and planVisible
Ordered procedure codeVisible
Full medical historyNot retrieved
Clinical notesNot retrieved
Your EHRPractice managementPayer portalsSelf-hosted n8n
Illustrative run. Fields and rules are set per workflow during scoping.
Inside your systems
EHR, practice management and payer portals
Clinician sign-off
On anything that touches a clinical decision
Your infrastructure
Self-hosted, so patient data stays in your environment
Every run recorded
Which record, which step, which person approved
Ovidius clients include
Where AI Agents Pay Off in Healthcare Administration
Start where the volume is high, the rules are written down, and a mistake gets caught by a person before it reaches a patient. These six workflows meet all three.
01 · Scheduling
Multi-practitioner scheduling
The agent places appointments across practitioners, rooms and locations, respecting treatment series, healing intervals and urgency limits. For one dental care group it schedules 1,500 appointments a week.
The agent reads intake forms, referral letters and faxed PDFs, fills the EHR fields, and asks your front desk only about the fields it could not read with confidence.
At booking, the agent checks the payer's rules for the ordered service, pulls the supporting documentation from the chart, and queues a complete packet for your staff to submit.
The agent answers scheduling and logistics questions, sends reminders and follow-ups, and hands anything clinical to a nurse with the conversation attached.
Before a visit, the agent pulls the relevant history into one summary. After it, the agent drafts referral letters and discharge summaries for the clinician to edit and sign.
Two US physician studies measured the desk work. Each figure below maps to the agent that takes it on.
2 hrs
of EHR and desk work for every hour of direct patient care
Sinsky et al., Annals of Internal Medicine, 2016
Taken on byIntake and chart prep agents
39
prior authorizations per physician each week
AMA Prior Authorization Physician Survey, 2024
Taken on byThe prior authorization agent
13 hrs
a week of physician and staff time spent completing them
AMA Prior Authorization Physician Survey, 2024
Taken on byPackets assembled from the chart at booking
94%
of physicians say prior authorization delays patients' access to care
AMA Prior Authorization Physician Survey, 2024
Taken on byAuth requirements flagged when the visit is booked
Why the admin queue is where to start
Administrative workflows have what an agent needs to run safely: high volume, written rules, and a person who reviews the output before it matters. A scheduling agent that proposes the wrong slot gets corrected at the front desk. That makes these workflows the place to put AI into production first, measure it, and earn the trust to go further. The AI Audit ranks your candidates by volume, risk and how cleanly your systems expose the data, so the first build is the one most likely to pay back.
HIPAA Compliant AI Starts With What the Agent Can See
HIPAA's minimum necessary standard limits each use of patient data to what the task requires. We enforce it in the architecture, before a prompt is ever built.
Your environment
Sources
EHR, practice management, payer portals
Read through the interfaces they already expose
Context layer
Scopes and masks
Minimum necessary fields per task, identifiers masked
Agent
Self-hosted n8n
Runs the workflow and its rules on your servers
Human review
Clinician or staff sign-off
Anything clinical or uncertain, before write-back
Run logrun 18342 · referral intake · 4 fields read · 1 sent to review · approved by front desk · written to EHR
Outside the boundary
Model provider
Receives scoped fields only
Under a business associate agreement, or replaced by a model you host
Never allowed
Raw charts pasted into prompts
Patient data used to train models
Clinical decisions without sign-off
Minimum necessary, enforced before the prompt
The common shortcut is to hand the model the whole chart and rely on it to ignore what it does not need, which fails the minimum necessary standard before the first answer. We put a context layer between your systems and the agent. It limits each task to the fields it may read, masks identifiers the agent does not need, and leaves the rest of the record where it is. A scheduling agent sees availability and treatment type. It never sees the clinical notes.
A clinician on every clinical decision
Anything that touches diagnosis, treatment or a symptom goes to a clinician with the context attached, and the agent cannot write it back until someone approves it. Administrative steps with clear rules run on their own, and every run records which record it read, what it changed, and who approved it. That record is what your compliance team reviews, and what our AI governance work is built around.
Built in your environment
We deploy on self-hosted n8n inside your cloud or data center, so the workflow, its logs and the patient data it touches stay in infrastructure you control. Where a workflow calls an external model, it sends scoped fields only, under a business associate agreement with that provider, or we run an open model on your own hardware. For the full architecture, read how we design context layers for protected healthcare environments.
Case Study: AI Scheduling for a Dental Care Group of 200 Practitioners
A Dutch Odoo implementation partner had spent a year trying to automate scheduling natively in Odoo for a dental care group serving 25,000 dependent residents. We built it as an agent on n8n instead.
Odoo + n8n · Dental care scheduling
A Dutch Odoo implementation partner, building for a dental care group across the Netherlands
Source
Odoo ERP
Practitioners, locations, treatment series
Nightly
Five sync workflows
Into a Supabase planning cache
n8n + AI agent
Multi-stage matching
The agent selects the best time slot
Exceptions
Failed-placement report
Explains why a slot was not found
The inverted logic
Illustrative week
Mon
Tue
Wed
Thu
Fri
Hygienist
Dentist A
Dentist B
Dentist C
Assistant
Unavailable: holiday, bookedPlaced by the agentNext session in a series
1,500
Appointments scheduled per week, processed nightly
200
Practitioners coordinated across multiple locations
25K
Dependent residents served across the network
5
Sync workflows between Odoo and Supabase
The challenge
Scheduling across 200 practitioners, multiple locations, treatment series with healing intervals, role matching and urgency limits created a web of interdependent rules that Odoo's data model was never designed to handle. A year of native development had not produced a working scheduler.
The solution
We left the scheduling logic out of Odoo and built an n8n orchestration layer on top of it, with Supabase as a planning cache. Every night, five sync workflows feed a multi-stage matching step, the agent selects the best slots, and any appointment it cannot place produces a report explaining why, so staff fix the exceptions instead of building the whole calendar. We inverted the logic: the engine records when practitioners are unavailable, and a calendar change becomes one new block. The partner now retains Ovidius for ongoing support and Phase 2. The full write-up is on our enterprise AI consulting page, and more results are in our case studies.
Ovidius did in weeks what we couldn't build natively in Odoo for a full year. They architected an AI-powered n8n layer on top of our ERP that now schedules well over a thousand appointments a week automatically. They are our preferred AI delivery partner.
Leadership, Dutch Odoo Implementation Partner
How a Healthcare AI Build Runs
Fixed fee, under 30 daysAuditOur AI Audit maps your workflows, the systems behind them and the data each agent would need, and ends in a 90-day roadmap ranked by return and risk.
Before you commitScopeOne workflow, the systems it touches, the fields it may read, the cases that go to a person, and what the build will take. You get the timeline before you sign anything.
Staging firstBuildWe build in staging against test or de-identified records, and your staff use it on real cases before anything goes live.
Monitored after launchShip and runSeparate development, pre-production and production environments, error alerts to our team, and monitoring by the engineers who built it. See how we work.
A healthcare IT consultancy recommends a vendor. A point solution gives you one workflow on its terms. We build the agent around your rules and run it in your environment.
Ovidius
Healthcare IT consultancy
Off-the-shelf AI tool
What you get
OvidiusWorking agents inside your EHR and practice systems
IT consultancyAn assessment, a vendor shortlist and an implementation plan
Off-the-shelf toolOne workflow, inside the vendor's product
Where patient data lives
OvidiusIn your environment, on self-hosted infrastructure
IT consultancyWherever the chosen vendor hosts it
Off-the-shelf toolIn the vendor's cloud
Fit to your rules
OvidiusBuilt around your scheduling, payer and escalation rules
IT consultancyConfigured within what the vendor supports
Off-the-shelf toolYour team adapts to the product
Timeline
OvidiusScoped per workflow, usually weeks
IT consultancyMonths of assessment before a build starts
Off-the-shelf toolQuick to switch on, limited to what the product does
After launch
OvidiusThe engineers who built it monitor and extend it
IT consultancyHanded to the vendor's support desk
Off-the-shelf toolThe vendor's roadmap decides what changes
Bring us the workflow your staff dread most.
We will tell you on the call whether an agent can take it on, and what it would take.
Frequently Asked Questions About AI Agents in Healthcare
How do you keep AI agents HIPAA compliant?
Three controls do most of the work. A context layer gives each agent only the fields its task needs, in line with HIPAA's minimum necessary standard. The workflow runs on self-hosted infrastructure in your environment, so patient data and logs stay with you. And any external model call carries scoped fields only, under a business associate agreement with that provider, or goes to a model you host.
Every run is logged with the record it read, what it changed and who approved it, which gives your compliance team something concrete to review. The governance and context layer pages cover the controls in more depth.
Do the agents make clinical decisions?
No. Agents prepare, sort and draft; a licensed clinician makes every clinical call. Anything touching diagnosis, treatment or a reported symptom goes to a person with the context attached, and the agent cannot act on it until someone approves. Fully automated steps are limited to administrative work with written rules, such as placing an appointment in an open slot.
Which EHR and practice management systems can you connect to?
We connect through whatever your systems expose: a FHIR or HL7 interface, a vendor API, a database you give us read access to, or, for older systems, structured exports and inbound documents such as faxed referrals. The dental scheduling build reads from Odoo through nightly sync workflows. The AI Audit confirms what each of your systems allows before anything is quoted.
How long does it take to build a healthcare AI agent?
It depends on the workflow. A single administrative workflow on systems with a clean interface usually takes weeks. One that spans several systems, or needs a context layer built first, takes longer. We scope each workflow and give you the timeline before you commit, and our roadmap service sequences several builds when you have more than one in mind.
What are the five types of AI agents, and which matter in healthcare?
The textbook five are simple reflex, model-based reflex, goal-based, utility-based and learning agents. In healthcare administration, the useful ones are the simpler types. Routing an inbound referral by type is reflex behavior. Placing an appointment against practitioner availability, treatment intervals and urgency is goal-based. Choosing the best of several valid slots is utility-based.
Production systems combine them inside one workflow, with fixed rules where the answer is known and a language model where the input is messy, such as a handwritten referral.
Where should a healthcare organization start with AI agents?
With the administrative workflow that has the most volume and the clearest rules, usually scheduling, intake or prior authorization. If you already know which one, book a discovery call and we will scope it. If you have several candidates, the AI Audit ranks them against your systems and data before you spend on a build.
Put Your First Healthcare Agent Into Production
Tell us which workflow eats your staff's week. We will map the systems it touches, the data it needs and the cases that stay with a person, and tell you what it takes to put an agent on it.
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