Your clinical staff spending hours on prior authorisations, referral coordination, and documentation that an AI agent could handle?
Implementing a healthcare chatbot that can only answer questions but cannot actually complete the workflow the patient or clinician needs?
AI Agents for Healthcare
Autonomous AI agents that handle specific healthcare workflows end-to-end -- prior auth, clinical documentation, care gap outreach, patient intake, and more.
Built for health systems, medical groups, and digital health companies that need agents taking actions in clinical workflows, not just answering questions.
Prior auth agents that extract criteria, match payer guidelines, submit requests, and track status
Clinical documentation agents that draft SOAP notes and encounter summaries from structured input
Care gap agents that identify patients, generate outreach, and track response
HIPAA-aware architecture with EHR integration via FHIR R4
RaftLabs builds autonomous AI agents for healthcare workflows -- prior authorisation processing, care gap identification, clinical documentation drafting, patient intake, and medication refill handling. Unlike chatbots that only answer questions, these agents take actions end-to-end within defined guardrails, integrating with EHR systems via FHIR and operating under HIPAA-aware architecture. Most healthcare AI agent projects deliver in 10-14 weeks at a fixed cost.
100+Products shipped
·HIPAAAware design
·FixedCost delivery
·10-14Week delivery
AI agents that act, not just answer
A chatbot responds to what a patient or clinician asks. An AI agent takes a workflow from start to finish -- gathering inputs, applying rules, calling systems, and producing outcomes. The difference matters in healthcare, where the cost of half-finished workflows lands on clinical staff.
We build healthcare AI agents with defined scope, explicit escalation logic, and HIPAA-aware data handling. Each agent handles one workflow well rather than many workflows poorly. Integration with your EHR is scoped during discovery because that is where most projects encounter unexpected complexity.
What we build
Prior authorisation agent
The agent extracts relevant clinical criteria from patient records, matches them against payer-specific guidelines, and submits authorisation requests to payer portals or clearinghouses. It then tracks submission status, follows up on pending requests, and flags exceptions that require a human reviewer -- incomplete documentation, criteria mismatches, or denials that warrant appeal. Clinical staff see only the exceptions. Routine submissions move without manual handling. The result is faster approvals and fewer write-offs from missed auth submissions.
Clinical documentation agent
The agent drafts SOAP notes and encounter summaries from structured input -- dictation transcripts, structured form responses, or clinical data already in the EHR. It pre-populates the relevant EMR template fields and flags data gaps for clinician review before the note is finalised. Clinicians review and sign rather than draft from scratch. Note generation is scoped to your documentation templates and reviewed by your clinical team before deployment. The agent does not generate clinical content outside the approved template structure.
Care gap and outreach agent
The agent queries your EHR or population health data to identify patients overdue for preventive care -- mammograms, colonoscopies, HbA1c checks, annual wellness visits -- or chronic disease follow-up milestones. It generates personalised outreach messages across configured channels (SMS, email, patient portal), tracks response, and schedules appointments for patients who engage. Non-responders are flagged for manual outreach review after a defined interval. The result is systematic care gap closure rather than manual list management.
Patient intake and triage agent
Before an appointment or telehealth visit, the agent collects structured intake data in a guided conversation -- chief complaint, symptoms, duration, severity, and relevant history. It screens for symptom patterns that suggest urgency and routes to the appropriate care level or flags for immediate clinical review. Collected data is pre-populated into the provider's pre-visit note in the EHR. Providers arrive at the appointment with structured intake already in front of them, not a blank screen and five minutes of information gathering.
Medication refill agent
The agent processes inbound refill requests against the patient's medication history, refill eligibility rules, and any formulary or quantity limits defined for the practice. Requests that meet all criteria generate a refill task for pharmacist or prescriber approval -- the agent handles the matching and documentation, not the clinical decision. Requests that do not meet criteria are flagged with the specific gap noted. The agent reduces the volume of refill tasks that require manual review from scratch while keeping clinical sign-off in place.
Appointment scheduling agent
The agent handles scheduling, rescheduling, and cancellation requests across provider calendars -- checking real-time availability from your scheduling system, applying appointment type rules, and booking directly. Cancelled slots trigger waitlist offers to patients who have indicated interest in earlier appointments. The agent handles confirmation and reminder messages and updates the EHR scheduling record. Front desk staff handle complex scheduling requests and relationship management; the agent handles volume.
Frequently asked questions
A chatbot responds to a query. An AI agent completes a workflow. When a patient asks a chatbot about refilling a medication, the chatbot gives instructions. When an AI agent handles a refill request, it checks eligibility, matches the request against refill rules, generates the refill task, and routes exceptions for review -- all without the patient needing to do anything beyond submitting the request. The distinction matters because most healthcare workflows have multiple steps across multiple systems. A chatbot stops at step one. An agent carries the workflow through to a defined outcome.
HIPAA compliance for AI agents requires the same controls as any PHI-handling system: Business Associate Agreements with every infrastructure provider processing PHI (including LLM API providers -- Anthropic, OpenAI, and major cloud providers offer BAAs for healthcare), encrypted data handling in transit and at rest, audit logging of every PHI access event, and minimum-necessary data access design. The agent accesses only the patient data the specific workflow requires. We document the data flows for each agent during development and include HIPAA compliance documentation with every project.
We integrate with EHRs that expose FHIR R4 APIs: Epic, Cerner/Oracle Health, Athenahealth, Allscripts, and most modern EHRs with FHIR support. For scheduling write-back and prior auth submission, integration depth depends on what each EHR's API supports for those specific workflows. For EHRs with limited API coverage, HL7 v2 feeds and direct database integrations are options. We confirm integration scope during discovery -- EHR integration is usually the highest-risk component of any healthcare agent build and must be scoped explicitly, not estimated generically.
A focused healthcare AI agent -- one workflow, one EHR integration, defined escalation logic -- typically runs $35,000--$75,000 and delivers in 10-14 weeks. A multi-agent system covering prior auth, documentation, and care gap outreach with deeper EHR integration runs $75,000--$175,000. Cost is driven by EHR integration complexity, the number of payer systems involved (for prior auth), and the clinical content scope that needs clinical team review before deployment. We scope and price every project before starting.
Talk to us about your healthcare AI agent project.
Tell us the workflow you want to automate, your EHR system, and the payer mix. We will scope what an agent can handle and give you a fixed cost.