AI Agents for Healthcare
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.
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
Recent outcomes
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SaaS · Logistics
2,000+ shipments yr 1
Multi-carrier shipping hub for Indonesian eCommerce
The problem
Clinical staff spending hours on prior authorisations and documentation an AI agent could handle?
Implementing a healthcare chatbot that can answer questions but can't actually complete the workflow?
Short answer
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.
Key takeaways
Trusted by
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. EHR integration is scoped during discovery, because that's where most projects encounter unexpected complexity.
Capabilities
Extracts clinical criteria from patient records via FHIR R4 resources, matches them against payer-specific guidelines, and submits requests via X12 278 or payer API. CDS Hooks lets the agent trigger directly from EHR workflow events. Status tracking escalates requests approaching payer deadlines automatically; clinical staff see only the exceptions, incomplete documentation, criteria mismatches, and viable-appeal denials.
Drafts SOAP notes and encounter summaries from dictation transcripts, structured forms, or existing EHR data, using clinical NLP to extract diagnosis and treatment information. Structured output validated against a JSON Schema confirms note content maps to your EHR template before pre-population. A human-in-the-loop checkpoint is mandatory: the clinician reviews, edits, and signs. The agent can't finalise documentation on its own.
Identifies care gaps against evidence-based screening guidelines (USPSTF, HEDIS measure logic) using FHIR CQL expressions your clinical staff can review without code changes. Outreach messages are generated per approved template and validated against a schema before multi-channel delivery (SMS, email, portal). Response tracking writes back to the patient record via FHIR; non-responders flag for manual follow-up after a configurable interval.
Collects structured intake data through a dynamic branching conversation, chief complaint, onset, severity, history, tailored to the presenting condition. Red-flag symptoms trigger immediate escalation rather than queuing for a standard appointment, against criteria your clinical team defines and the agent applies, not sets. Collected data writes to the EHR as FHIR resources, pre-populating the provider's pre-visit note.
Checks refill requests against medication history, eligibility rules, controlled substance requirements, and required monitoring labs in seconds. Requests meeting criteria generate a structured task for pharmacist or prescriber approval with everything needed to confirm in 15 seconds. Requests failing eligibility are flagged with the specific gap, "refill requested 5 days early," not a generic denial.
Handles scheduling, rescheduling, and cancellation using real-time availability and your configured appointment-type rules. Cancellations more than 24 hours out trigger a waitlist offer, typically recovering 30-50% of cancelled slots as rebooked visits. The agent escalates complex multi-provider scheduling and clinically-sensitive requests to front desk staff, handling the 60-80% of routine volume itself.
How we work
We map the target workflow, your EHR's API coverage, and escalation logic. You leave week 1 with a written scope document and a fixed-price quote.
BAA coverage, data flow, and human-in-the-loop checkpoints designed and documented before any PHI flows through the system.
Agent workflow built against LangGraph, tested against real EHR sandbox data, and reviewed by your clinical team before go-live.
Production deployment with audit logging active from day one and escalation queues staffed and ready.
Why us
The engineers who assess your workflow also build the solution. No bait-and-switch, no offshore handoff after the contract is signed.
We scope the work, calculate the cost, and lock it in writing before any development starts.
Clients include Vodafone, T-Mobile, Aldi, Nike, Cisco, and Lockheed Martin. Track record shipping HIPAA-compliant systems for US healthcare clients.
Every clinical decision, note finalisation, and triage redirect requires a mandatory checkpoint. The agent assembles the context; a clinician makes the call.
We don't estimate EHR integration generically. API coverage variance between Epic, Cerner, and Athenahealth configurations is large enough to change project scope materially, so we confirm it during discovery.
Tell us the workflow you want to automate, your EHR system, and the payer mix. We'll scope what an agent can handle and give you a fixed cost.
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Read moreA chatbot responds to a query. An AI agent completes a workflow end-to-end: gathering inputs, applying rules, calling systems, and producing outcomes. Agents operate as stateful, multi-step processes that can call external systems and take actions, modelling the workflow as a directed graph with explicit state, tools, and decision branches. In healthcare, this matters because workflows like prior authorisation, care gap outreach, and medication reconciliation span multiple systems, have exception conditions requiring clinical judgment, and must produce auditable records of every action taken.
HIPAA compliance for AI agents requires Business Associate Agreements with every infrastructure provider processing PHI, encrypted data handling in transit (TLS 1.2 minimum) and at rest (AES-256), audit logging of every PHI access event, and minimum-necessary data access design. LLM API providers such as Anthropic, OpenAI, and major cloud providers offer BAAs for healthcare customers, but PHI cannot be sent to LLM APIs without one in place. The vector database used for clinical knowledge retrieval is deployed within a HIPAA-eligible environment with network isolation.
We integrate with EHRs that expose FHIR R4 APIs: Epic (via App Orchard and Open API programmes), Oracle Health (Cerner) via Ignite APIs, Athenahealth via Marketplace API, Allscripts, ModMed, and most ONC-certified modern EHRs. CDS Hooks integration is available on Epic and Cerner where the practice has licensed the capability. For scheduling write-back and prior auth submission, integration depth depends on what each EHR's API supports. For EHRs with limited FHIR coverage, HL7 v2 feeds are the fallback path.
A focused healthcare AI agent covering one workflow, one EHR integration, and HIPAA-compliant architecture typically runs $35,000-$75,000 and delivers in 10-14 weeks. A multi-agent system covering prior auth, clinical documentation, and care gap outreach with FHIR R4 write-back and a vector database for clinical knowledge retrieval typically runs $75,000-$175,000. Cost is driven by EHR integration complexity, the number of payer systems involved, and the clinical content scope requiring clinical team review before deployment.
Work with us
We scope AI Agents for Healthcare in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.