Patient Intake and Triage Agents
Collect demographics, insurance and symptoms by chat, SMS, portal or voice, screen for red flags with deterministic rules, apply licensed protocols such as Schmitt-Thompson, and route to a nurse or scheduler.
Healthcare AI agent development
AI Agents for Healthcare: Custom Development and EHR Integration
We design, build and run AI agents for providers, payers and digital health companies. Each agent works through your EHR, clearinghouse and scheduling systems with scoped permissions, a human approval step where the stakes call for one, and an evaluation suite that gates every release.
For CTOs, CMIOs, revenue cycle leaders and healthtech product teams in the US.
Updated September 2026
AN AGENT IS NOT THE END GOAL.A workflow that
works better is.
works better is.
Connected systems. Accountable actions.
What an AI Agent for Healthcare Is, and What We Build
An AI agent for healthcare is software that uses a language model to understand a task, calls approved tools such as FHIR APIs, X12 transactions and scheduling services to gather context and act, and hands decisions that need clinical or financial judgment to a named person. We build agents for patient intake and triage, ambient clinical documentation, prior authorization, eligibility verification, denial management, patient communication and chronic care management. The outcome is a workflow your staff trust: fewer manual steps, every action logged, and results written back to the systems of record.
Types of Healthcare AI Agents We Build
Each agent type has its own systems, standards and approval boundary. Start with one, prove it in shadow mode, then extend.
Clinical Documentation and Ambient Scribe Agents
Capture the encounter with consent, draft a SOAP, H&P or behavioral health note in the clinic's template, link every sentence to its source, suggest ICD-10-CM and E/M codes, and write the signed note back to the chart.
Prior Authorization Agents
Check whether a service needs authorization, gather the documentation the payer's criteria ask for, pre-fill Da Vinci DTR questionnaires, and submit through Da Vinci PAS or the X12 278, with a person approving every submission.
Prior authorization automation Prior auth agent architecture
Eligibility and Benefits Verification Agents
Run X12 270/271 inquiries before the visit, read coverage, copay, deductible and plan dates from the 271, flag inactive coverage and coordination of benefits issues, and post the result to the appointment.
Denial Management and Appeals Agents
Parse 835 remittances and CARC/RARC codes, group denials by root cause, pull the chart evidence an appeal needs, draft the appeal letter against the payer's policy, and queue it for a biller to approve.
Patient Communication and Virtual Assistant Agents
Answer questions from approved content, book and reschedule appointments against open slots, take refill requests for staff review, and send reminders, with identity checks before any PHI is shown.
Care Management and Remote Monitoring Agents
Prepare care coordinator worklists, triage device readings against thresholds your care team sets, draft outreach, track CCM and RPM time, and keep the care plan current for the billing audit trail.
AI chronic care management agent Chronic care management software
Imaging Workflow Agents
Route studies to FDA-cleared imaging models, return results as DICOM SR and SEG objects to PACS, reprioritize the radiologist worklist, and track follow-up recommendations for incidental findings.
What the agent may do, and who stops it
How a Healthcare AI Agent Runs One Task, Asks Permission and Fails Safely
An agent differs from a chatbot because it acts. So the useful questions are what it may do alone, what it must ask about, and what happens when it is wrong. This reference design shows one agent run from trigger to audit, and how it can earn more freedom over time.
Reference architecture: one agent run, from trigger to audit
Trigger
- HL7 ADT or FHIR eventA patient is discharged or a claim is denied
- Staff requestFrom a work queue, with the user's context
- Patient messageTreated as untrusted input
Each run starts with a named workflow, a named patient or claim, and a task identity
Agent logic
- Task planA fixed workflow with limited model choice
- ModelBAA-covered endpoint, version recorded
- Deterministic rulesRed flags and eligibility checks, outside the model
The model can only ask for a tool. It cannot call a system directly
Agent gateway
- Tool permission checkThis task, this patient, this action
- Schema and source checksMalformed output or missing source links are refused
- Spend and step limitsA stuck run stops and calls a person
Allowed reads run. Consequential writes wait for a person
Approval and tools
- Approval queueDraft, evidence and a plain reason
- FHIR toolsSMART Backend Services, narrow scopes
- X12 tools270, 276, 278 and 837 via your clearinghouse
Each call and approval goes to the run record
Oversight
- Replayable run recordInputs, tools, versions and approver
- Override and escalation ratesReviewed by the workflow owner
- Kill switchTurns off one agent or one tool
Permission levels: what an agent may do alone
Set these per action, with your workflow owner. The examples show a cautious starting point, and you can move a line only when evidence supports it.
LevelExampleStarting rule
LevelRead
ExampleLook up coverage or last visit
Starting ruleAutomatic, limited to the task's patient, and logged
LevelPrepare
ExampleDraft an appeal letter
Starting ruleAutomatic, and shown as a draft
LevelLow-risk send
ExampleAppointment reminder with no clinical content
Starting ruleAutomatic from approved templates
LevelConsequential write
ExampleUpdate the chart, submit a claim or prior auth
Starting ruleA person approves each one
LevelClinical judgment
ExampleTriage, diagnosis, or a treatment decision
Starting ruleThe agent does not decide. It routes to a clinician, and red flags use fixed rules
Workflow: a denied claim, from 835 to a signed appeal
835 event
Denial arrivesCARC and RARC codes open a run for that claim.Agent
Classifies the denialFixed rules sort known codes. The model helps with unclear remarks.If this is a type outside its scope: it goes straight to a biller with a note.
Agent
Gathers evidenceReads the claim, note and policy, using read-only tools.Gateway
Treats documents as dataTool permissions sit outside the model, so an instruction hidden in a payer letter cannot grant the agent new ones.If a known injection pattern appears: the run stops and flags it. Pattern checks catch only some attempts, which is why the permissions matter more.
Agent
Drafts the appealEach statement links to its source. Those with no link are held back, and a link still needs a person to check it.Denials specialist
Reviews and signsEdits, approves or rejects. Reasons feed the test set.Gateway
Submits and watchesSends through the approved route, and a retry key prevents a duplicate.Workflow owner
Reviews overridesCommon edits become fixes, tests or rule changes.
Delivery process: how an agent earns more autonomy
Level 0: Tools only
Connect typed tools to sandboxes, with no model acting.
- Gate passes when
- Each tool passes success, failure and refusal tests.
- You hold
- Tool contracts and test results.
Level 1: Shadow
Runs on real work without acting, beside your staff.
- Gate passes when
- Results match the bar your owner set.
- You hold
- Comparison report and test set.
Level 2: Assist
Staff approve every proposed action.
- Gate passes when
- Override rate and reasons are acceptable to the owner.
- You hold
- Approval logs and an override report.
Level 3: Limited autonomy
Only named low-risk actions run alone, with sampling.
- Gate passes when
- Sampled audit stays within the bar.
- You hold
- Permission list, monitoring and a kill switch.
Risks specific to agents, and how to test the control
A chatbot can say something wrong, while an agent can do something wrong. So test the failures as well as the good runs.
RiskControlTest to ask for
RiskPrompt injection from a document or message
ControlContent treated as data, and tool permissions fixed outside the model
Test to ask forA planted instruction in a test fax
RiskActs on the wrong patient
ControlPatient bound to the run at the start, and checked by every tool
Test to ask forA request naming a different patient
RiskRepeats an action on retry
ControlUnique keys on every write
Test to ask forKill the run between send and confirm
RiskRuns forever and costs too much
ControlStep and spend limits
Test to ask forA task with no possible finish
RiskApprovers click through
ControlSample reviews and seeded test items
Test to ask forReport on review time and catch rate
RiskA clinical emergency reaches a chat agent
ControlFixed red-flag rules and an immediate handoff
Test to ask forScripted emergency messages
PHI in agent runs: where it may be stored
PHI, under a BAA
- Model endpoint with no training on your data
- Run records, access-restricted with set retention
- Approval queue, role-limited
IDs only
- Operational logs and traces
- Cost and latency dashboards
- Alerts sent to engineers
Blocked
- Agent memory shared between patients
- Web search or tools outside the approved list
- Test sets that have not been de-identified
Deliverables and ownership questions for agent projects
Once paid, you own the custom code we write for you and all of your data. We keep our reusable tools and open-source parts. For agents, go beyond code, because these are the parts that took the longest to get right.
Evaluation suite
Test cases written by your clinicians or billers should stay usable with another model or vendor.
Tool contracts
Documented interfaces, so another team or framework can use them.
Permission policy
A readable list of what each agent may do, which your governance team approves.
Run history
Who holds it, how long it stays, and whether you can export it if you leave.
Evidence, with its limits
You will see large interaction counts on other sites. We do not publish those. Here is what you can look at directly.
- Open-source Da Vinci CRD, PAS and DTR servers tested with InfernoWhat it does not show: Agent accuracy, or live payer approval. It shows the prior authorization standards an agent would use.Read the code ↗
- Open-source X12 clearinghouse simulator: 270/271, 278, 837, 999/277CA, 835 and 276/277What it does not show: A live payer connection. It gives agents a safe place to fail in test.Read the code ↗
- SMART on FHIR app run against the Epic and Oracle Health developer sandboxes in one sessionWhat it does not show: Agent write access in production, which needs vendor and customer approval.Watch the recording ↗
How to judge a healthcare AI agent vendor
Interaction counts tell you about volume, and little about safety. Ask to see these for your workflow.
- Show me the permission list for this agentIf it cannot be printed, it may only exist in a prompt.
- Plant an instruction in a test documentSee whether the agent follows it, and what the log shows.
- Replay a run from last weekYou should see inputs, tools, versions and who approved.
- Turn it off, right nowAsk who can, how, and what happens to work in progress.
- Tell me about a case where it should not actA vendor that claims it handles everything has not looked hard at your workflow.
Start from one workflow
Pick one workflow with a clear owner, and tell us which systems it touches. We will talk through where the approval lines should sit, and whether an agent is the right tool at all.
Talk through one workflow- The workflow owner and today's baseline
- The systems and data the agent would need
- Which actions must always have a person
Healthcare AI Agent Architecture: Tools, Guardrails and Evaluation
The model is one component. What makes an agent safe in a clinic or a billing office is the layer around it.
Tool Calling Over FHIR, X12 and HL7 V2
Agents act only through typed tools behind an agent gateway: FHIR R4 reads and writes authorized with SMART Backend Services, X12 270, 276, 278 and 837 through your clearinghouse, HL7 v2 ADT events as triggers, and CDS Hooks for in-workflow cards. Tools can be exposed over the Model Context Protocol so the same contracts serve any model.
Human in the Loop by Action Type
Reads are automatic. Writes that change the chart, a claim or a patient message wait in an approval queue with the evidence attached. Each tool carries an approval policy, so a reminder can send on its own while an appeal or a note needs a signature.
Guardrails in Code, Not Prompts
Safety-critical logic such as red flags, eligibility rules and payer criteria runs as deterministic rules. Model output is validated against a schema, claims must cite a source record, prompt injection is filtered at every tool boundary, and out-of-scope requests route to a person.
PHI Handling
We sign BAAs. Models run on endpoints covered by a BAA with no training on your data, such as Amazon Bedrock, Azure OpenAI Service or Google Vertex AI, or on models hosted in your own cloud. PHI is encrypted in transit and at rest, logs carry IDs rather than clinical text, and access follows the minimum necessary standard. Nirmitee is ISO 27001:2022 certified.
Evaluation Before and After Release
Every agent ships with a clinician- or biller-authored test set, regression gates on each prompt and model change, red-team cases for injection and PHI leakage, a shadow period against live work, and production monitoring of override rates, escalations and drift.
Audit Trail and Observability
Each run records the input, the tools called with their responses, the model and prompt versions, the output, and who approved or overrode it. Reviewers can replay any run, and dashboards show cost, latency and failure modes per workflow.
EHR Integration for Healthcare AI Agents
Agents are only as useful as their read and write access. Each EHR exposes different FHIR write support, so we map every tool to what the target system accepts.
Epic
FHIR R4 APIs registered through Epic's developer program, SMART on FHIR launch for clinician-facing agents, Backend Services for background agents, CDS Hooks for in-workflow suggestions, and HL7 v2 or Bridges interfaces where a FHIR write is not offered.
Oracle Health (Cerner)
Millennium FHIR R4 APIs for patient context, documents and scheduling, SMART launch inside PowerChart, and HL7 v2 interfaces for results and ADT events that trigger agent work.
athenahealth
athenahealth's FHIR R4 endpoints plus its proprietary APIs, which cover appointments, clinical documents, claims and patient cases that FHIR alone does not reach.
eClinicalWorks
eClinicalWorks FHIR APIs for read access, and HL7 v2 or document interfaces for write-back, which is the common path for ambulatory notes, orders and results.
See Interoply, our integration platform for FHIR, HL7 v2 and X12
Regulatory Boundaries for AI Agents in Healthcare
We design each agent so its intended use sits on the right side of these rules, and we document the mapping for your compliance and clinical governance teams.
FDA Clinical Decision Support Guidance
Section 520(o)(1)(E) of the FD&C Act excludes some clinical decision support software from device regulation when it supports a clinician who can independently review the basis of a recommendation. Agents that analyze images or signals, or that direct patients to act, fall outside that exclusion and are regulated as software as a medical device.
HTI-1 Decision Support Interventions
ONC's HTI-1 final rule created the Decision Support Interventions criterion at 45 CFR 170.315(b)(11). Certified health IT must expose source attributes for evidence-based and predictive DSIs, and developers of predictive DSIs must run intervention risk management. Agents delivered inside a certified EHR inherit these expectations.
Section 1557 and Patient Care Decision Support Tools
The 2024 Section 1557 rule (45 CFR 92.210) requires covered entities to identify patient care decision support tools that use race, color, national origin, sex, age or disability as inputs, and to mitigate the risk of discrimination. Our evaluation plans include subgroup analysis for that reason.
State AI Laws
California AB 3030 requires a disclaimer and a path to a human on generative AI patient communications about clinical information unless a licensed provider reviews them. Texas HB 149 requires providers to disclose AI use in treatment, Colorado SB 24-205 sets duties for high-risk AI in healthcare decisions, and Illinois restricts AI in psychotherapy. We build disclosure, review and opt-out paths as configuration per state.
Payer Rules for Coverage Decisions
CMS requires Medicare Advantage coverage decisions to rest on the individual patient's circumstances (CMS-4201-F), and CMS-0057-F sets FHIR prior authorization API deadlines in January 2027. Payer-side agents prepare and summarize; a qualified reviewer makes any adverse determination.
HIPAA and 42 CFR Part 2
Agents follow the minimum necessary standard, run under BAAs with every subprocessor, and respect the separate consent rules for substance use disorder records under 42 CFR Part 2 and for psychotherapy notes.
Build vs Buy a Healthcare AI Agent
Most organizations combine options. The question is where your workflow differs enough that a packaged agent will not fit.
| Option | Fits when | Watch for |
|---|---|---|
| EHR-native AI features | The workflow lives entirely inside one EHR and the vendor's roadmap covers it. | Limited control over prompts, evaluation and data outside the EHR; release timing follows the vendor. |
| Point-solution vendors (ambient scribes, prior auth, contact center) | A common workflow such as primary care documentation with standard templates. | Per-seat or per-transaction pricing at scale, specialty gaps, and integration depth that varies by EHR. |
| Cloud agent platforms (Amazon Bedrock Agents, Azure AI Foundry, Vertex AI Agent Builder) | You have an engineering team and want managed orchestration under your cloud BAA. | The healthcare work remains yours: FHIR and X12 tools, guardrails, evaluation and approvals. |
| Custom build with Nirmitee | The agent is part of your product, spans several systems, or needs specialty logic, payer rules or your own approval model. | Needs an internal owner for the workflow and an agreed evaluation baseline before build. |
Deliverables
What You Receive from a Healthcare AI Agent Engagement
Once paid, you own the custom code we write for you and all of your data. We keep our reusable tools and open-source parts. Custom code, infrastructure, test sets and documentation live in your repositories.
- Workflow map with the approval boundary for every action
- Tool contracts for each FHIR, X12 and HL7 v2 interface
- Agent service with gateway, guardrails and approval queue
- Evaluation suite, test results and a documented go or no-go
- Intended-use statement and regulatory mapping for your governance team
- Monitoring dashboards, runbook and rollback switch
- Custom source code and infrastructure as code in your repositories
Healthcare AI Agent Delivery Phases and Timeline
- 012 to 3 weeks
Discovery and Workflow Mapping
Confirm the workflow owner, systems, data access, approval points and the baseline you will measure against.
- 024 to 6 weeks
Connected Prototype
Build the agent against EHR and clearinghouse sandboxes with real tool contracts, then run the first evaluation set.
- 033 to 6 weeks
Evaluation and Shadow Mode
Run beside your staff on live work without acting, compare outcomes, tune rules and prompts, and sign off the release gate.
- 042 to 4 weeks, then ongoing
Staged Go-live and Operations
Release to one site or queue, review overrides weekly, then extend. We can run the agent or hand it to your team.
What Drives the Cost of a Healthcare AI Agent
Number of Systems and Write Paths
Each EHR, clearinghouse or payer adds tool contracts, sandbox access and certification steps. Writes cost more than reads.
Approval and Safety Design
Clinical agents need protocol encoding, red-flag rules and review screens. Back-office agents need payer rule logic and exception queues.
Evaluation Depth
The size of the test set, clinician review time, subgroup analysis and the length of the shadow period.
Model Hosting Choice
Hosted models under a BAA are faster to start. Self-hosted models in your cloud add GPU infrastructure and MLOps.
Channels
Voice adds telephony, speech recognition and latency work on top of chat or portal channels.
Run Model
Whether we operate the agent with monitoring and on-call, or hand it to your team with a runbook.
Proof from Our Healthcare AI and Interoperability Work
Client names are withheld. Each item below is live work or a public repository you can inspect.
- ISO 27001:2022 certified
- We sign BAAs
Behavioral Health AI Scribe, Live
An assistive scribe inside a US behavioral health practice group's clinical platform. It drafts progress notes in SOAP, DAP or BIRP format, and a licensed clinician accepts or edits each section before signing.
Nurse Voice-to-chart, Live
A mobile app for a long-term care operator: a nurse speaks the assessment, and it returns as discrete fields for vitals, pain and skin that the nurse confirms before they reach the chart.
Oncology Patient Copilot, Live
A patient app that answers by voice or chat only from the patient's own record and a cited clinical guideline, and hands anything else to the care team.
Open-source Da Vinci CRD, DTR and PAS Servers
Prior authorization servers tested with the ONC Inferno test kits, the same rails a prior auth agent calls.
Headless EHR on FHIR
Our open-source headless EHR passes 47 of 47 ONC (g)(10) SMART App Launch STU2 standalone patient tests in Inferno.
Interoply Integration Platform
Our integration platform connects EHRs and payers over FHIR R4, HL7 v2 and X12, with a full run log and a task inbox for the exceptions a person must decide: the rails our agents call.
EVIDENCE FOR YOUR SHORTLISTReal Healthcare Challenges.
Real Healthcare Challenges.
See How We Approached Them.
Evaluating an integration or product partner? Explore the systems, workflows, and technical decisions behind our work.
Explore all case studies
Case Study / 01
AI Progress Notes Case Study: 81 Notes Signed by Therapists in 11 Days
81 AI-drafted therapy notes signed in 11 days, 68% with no edits, $0.14 a draft. How we built it, the safety rules, and the time-saved formula.
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Case Study / 02
Multi-Source Data Integration Platform: The Context Layer Behind Governed AI
How Nirmitee built a multi-source data integration platform for a vaccine maker: 54 live integrations, human approval and AI answers traced to their sources.
Explore the implementation
Case Study / 03
Clearinghouse Claims Integration: Hardening a Live X12 Pipeline for a US Behavioral-Health EHR
How Nirmitee hardened a live clearinghouse claims integration for a US behavioral-health EHR, tracing 837P, 835 and 277CA defects to root cause and fixing them.
Explore the implementationWorking through a similar challenge?Discuss your requirements with our team
Explore specific possibilities
A closer look at
healthcare agent use cases.
Browse the existing agent library to guide your conversation. Suitability, availability, validation, and integration scope need assessment for your environment.
7 use cases
Radiology & Imaging
Diagnostic Imaging AI Integration
Explore this use case Clinical IntelligenceClinical Decision Support System
Explore this use case Patient Access & TriageAI Patient Triage Software
Explore this use case Patient AccessAI Virtual Health Assistant
Explore this use case Care ManagementAI Chronic Care Management Agent
Explore this use case Clinical DocumentationAI Agents for Clinical Documentation
Explore this use case Clinical ResearchClinical Trial Optimization AI
Explore this use caseHealthcare AI Agent Guides from Our Engineers
- Agentic AI in healthcare: complete guide
- How AI agents integrate with EHR systems
- Connecting agents with FHIR, CDS Hooks and SMART
- Top use cases for AI agents in healthcare
- Prerequisites before building a healthcare AI agent
- When a rules engine beats an AI agent
- AI agents and X12 270, 271, 278 and 835
- Healthcare AI implementation playbook
FAQs
AI Agent for Healthcare: Common Questions
What is an AI agent in healthcare?
An AI agent in healthcare is software that uses a language model to interpret a task, calls approved tools such as FHIR APIs, X12 transactions and scheduling services, and completes a workflow step or prepares it for a person to approve. Unlike a chatbot, it acts on systems of record under defined permissions and leaves an audit trail.
What are the main use cases for AI agents in healthcare?
The most common are patient intake and triage, ambient clinical documentation, prior authorization, eligibility verification, denial management and appeals, patient scheduling and communication, chronic care management and remote monitoring, and imaging workflow. Back-office revenue cycle agents are usually the fastest to prove because the inputs and outcomes are structured.
How is an AI agent different from a healthcare chatbot or RPA?
A chatbot answers questions. RPA repeats fixed clicks on a screen. An agent reasons over context, chooses which approved tool to call, handles variation in documents and payer responses, and escalates when it is unsure. Many good designs combine all three, with deterministic rules owning the safety-critical steps.
How do AI agents integrate with Epic, Oracle Health, athenahealth and eClinicalWorks?
Through each EHR's FHIR R4 APIs, authorized with SMART on FHIR for clinician-facing agents and SMART Backend Services for background agents, plus CDS Hooks for in-workflow cards. Where the EHR does not accept a FHIR write, we use the vendor's own APIs or HL7 v2 interfaces for write-back.
Are healthcare AI agents HIPAA compliant?
An agent is HIPAA compliant when the whole system is: BAAs with every party that touches PHI including the model provider, encryption in transit and at rest, minimum necessary access per tool, PHI kept out of logs, and audit trails. We sign BAAs, run models on BAA-covered endpoints with no training on your data, and Nirmitee is ISO 27001:2022 certified.
Does the FDA regulate healthcare AI agents?
It depends on intended use. Administrative agents for scheduling, eligibility or billing are not medical devices. Clinical decision support can fall outside device regulation when a clinician can independently review the basis of each recommendation, under section 520(o)(1)(E) and FDA's CDS guidance. Agents that analyze images or signals, or direct patients to act, are regulated as software as a medical device.
How do you keep a healthcare AI agent from making unsafe decisions?
Safety-critical logic runs as deterministic rules rather than prompts, each tool carries an approval policy, outputs are validated against a schema and must cite source records, and uncertain cases route to a person. A release ships only when it passes a clinician- or biller-authored evaluation suite, and a shadow period compares it with live staff decisions first.
Should we build or buy a healthcare AI agent?
Buy when the workflow is common and lives inside one system, such as primary care ambient documentation with standard templates. Build when the agent is part of your product, spans several systems, needs specialty or payer-specific logic, or must follow your own approval model. Many organizations buy for commodity workflows and build where they differ.
How long does it take to build a healthcare AI agent?
A single-workflow agent typically takes 11 to 19 weeks from discovery to staged go-live: 2 to 3 weeks of discovery, 4 to 6 weeks for a connected prototype, 3 to 6 weeks of evaluation and shadow mode, and 2 to 4 weeks for staged release. EHR vendor app review and clearinghouse enrollment can extend that.
What does a healthcare AI agent cost to build?
Cost depends on the number of systems and write paths, the approval and safety design, evaluation depth, model hosting, channels such as voice, and whether we operate the agent afterwards. We scope a fixed discovery first and price the build once the systems and approval points are known.
Which language models do you use for healthcare agents?
We choose per workflow: frontier models through Amazon Bedrock, Azure OpenAI Service or Google Vertex AI under your cloud BAA for reasoning-heavy work, and smaller or self-hosted models where cost, latency or data residency matter. Tools and evaluations are model-agnostic, so a model change is a tested release, not a rebuild.
How do you measure whether a healthcare AI agent is working?
We agree a baseline before build and track workflow measures such as turnaround time, touches per case, first-pass acceptance, override rate with reasons, escalation accuracy and error types. Clinical agents add safety measures such as red-flag sensitivity and omission rates in drafted notes.
Start with one workflow
Scope Your First Healthcare AI Agent
Bring one workflow, the systems it touches and the result you want. In a 30-minute call we will map the tools, approval points and evaluation plan, and tell you what the first release needs.
Book a scoping call Send us your workflow ↗No patient records are needed for the first conversation. We sign a BAA before any PHI is shared.
From our YouTube channel
Related videos
Explore the workflows and decisions behind this service. Demonstrations and recorded discussions have the scope described in each video.
Agentic RCM Denial Management Demo | Coverage DeskWatch on YouTube
A simulated denial-management workflow using fictional clinic records and payer responses.
Epic Integration for AI Scribes: Reviewed Note to Verified Write-Back (Live Sandbox)Watch on YouTube
Epic sandbox note filing and verification. Drafting uses a fixed template with synthetic data; the lost reply is injected.