AI remote patient monitoring for chronic care
- 20%
- less clinical decision-making time
- 150+
- patients in 12 weeks
AI in Healthcare Agent Development
AI in healthcare that completes the workflow, not just answers it.
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
Bring the problem, the current workflow, or the existing code. We reply with a practical next step within one business day.
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The brief
Start with what is not working.
Good software decisions begin with the constraint, not a list of features or a preferred technology.
01
Clinical staff spending hours on prior authorisations and documentation an AI agent could handle?
02
Implementing a healthcare chatbot that can answer questions but can't actually complete the workflow?
Plain answer
AI in healthcare, at RaftLabs, means autonomous 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. A focused build launches a validated v1 in 10-14 weeks at a fixed cost.
What to remember
- Agents are stateful, multi-step processes built on frameworks like LangGraph, modelling a workflow as a directed graph with explicit state, tools, and human-in-the-loop checkpoints.
- Every agent's PHI access requires a BAA with the infrastructure and LLM provider, encryption in transit and at rest, and an audit trail per 45 CFR 164.312.
- EHR integration depth (Epic App Orchard, Cerner Ignite) is the highest-risk, highest-variance component and is scoped explicitly, never estimated generically.
- A focused single-workflow agent runs $35,000-$75,000; a multi-agent system across prior auth, documentation, and outreach runs $75,000-$175,000.
The prior auth queue that stopped landing on your nurses.
A prior authorisation used to move one way. A nurse pulls the chart, reads the payer criteria, assembles the packet, submits it, then chases the status for days. Multiply that by every request, and your most expensive clinical staff spend their afternoons on paperwork.
Now an agent takes the request first. It extracts the criteria from the record, matches the payer guideline, submits the packet, and tracks the deadline on its own. What reaches a nurse is the exception: a criteria mismatch, missing documentation, a denial worth appealing.
A chatbot could have told the nurse the policy. It could not have filed the request. That gap is the whole point.
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. The American Medical Association's prior authorization survey found physicians and their staff spend an average of about 13 hours a week on prior authorizations alone, the kind of structured, high-volume workflow an agent is built to carry. McKinsey estimates AI can automate 50 to 75% of the manual tasks inside prior authorization (McKinsey), which is why it is the workflow most healthcare teams point an agent at first. This is the engineering discipline behind AI agent development, applied to the clinic.
Many teams arrive with a demo already built. An agent scaffolded in an afternoon with an AI tool answers cleanly on happy-path data. Then it meets a live EHR, real PHI, and an audit trail it was never designed for. We assess what you have, keep the workflow logic that holds, and rebuild the parts that won't survive real patient data. Where the job is broader than one agent, we bring in AI for healthcare and healthcare software development so the agent fits the systems around it.
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. 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 before we quote.
The team that scopes your workflow is the team that ships it. No bait-and-switch, no handoff after the contract is signed. We scope the work, calculate the cost, and lock it in writing before any development starts. A focused agent launches a validated v1 in 10-14 weeks, then grows as you add workflows.
An agent pays off when the workflow is repeatable and the record is the source of truth.
Everything on the left should already be true for your practice. Even one thing on the right, and a plain chatbot is the smarter first step.
A fit
01
A high-volume clinical or administrative workflow, prior auth, documentation, or care gap outreach, that your staff run the same way every day.
02
An EHR that exposes FHIR R4 APIs (Epic, Oracle Health/Cerner, Athenahealth) for the agent to read from and write back to.
03
You need the agent to complete the workflow end-to-end within HIPAA-aware guardrails, not just answer questions.
Not a fit
01
You need a simple FAQ responder or patient chatbot, not a system that takes action.
02
The workflow depends entirely on clinical judgment the agent was never given context for.
03
Your EHR exposes no FHIR or HL7 interface to read from or write back to.
What we build
Agents scoped to one healthcare workflow each
- 01Prior authorisation agentExtracts 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. Built on LangGraph.
- 02Clinical documentation agentDrafts SOAP notes and encounter summaries from dictation transcripts, structured forms, or existing EHR data, using BioBERT 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, then writes back as a FHIR R4 DocumentReference. A human-in-the-loop checkpoint is mandatory: the clinician reviews, edits, and signs. The agent can't finalise documentation on its own.
- 03Care gap and outreach agentIdentifies 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. Clinical knowledge retrieval is grounded in a Pinecone vector store.
- 04Patient intake and triage agentCollects 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.
- 05Medication refill agentChecks 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.
- 06Appointment scheduling agentHandles scheduling, rescheduling, and cancellation using real-time availability and your configured appointment-type rules. Cancellations more than 24 hours out trigger a waitlist offer, so an open slot gets rebooked instead of lost. The agent handles routine booking itself and escalates complex multi-provider and clinically-sensitive requests to front desk staff. Where the routine-to-exception split lands, and how much slot recovery you gain, depends on your no-show baseline and appointment mix, so we measure both against your own numbers rather than quote an industry figure.
Scheduling bot vs clinical-aware agent vs front-desk staff
| What happens | Scripted scheduling bot | Clinical-aware agent | Front-desk staff |
|---|---|---|---|
| Books a routine appointment | Yes, on a fixed script | Yes, against your appointment-type rules | Yes, but slow at volume |
| Reads the record via FHIR | No | Yes, scoped to minimum-necessary PHI | Manually, one chart at a time |
| Handles a red-flag symptom | Routes it blindly | Escalates against clinician-defined criteria | Depends on training and load |
| Writes back to the EHR | No | Yes, as a FHIR resource with an audit trail | By hand |
| Knows when to stop | No sense of exceptions | Hands exceptions to a human | Always human |
Key Insight
The regulatory clock is already running. Under the CMS Interoperability and Prior Authorization Final Rule, impacted payers must return standard prior authorization decisions within seven calendar days and urgent ones within 72 hours, and the FHIR-based Prior Authorization API requirement takes effect January 1, 2027 (CMS). Agents that read and write prior auth over FHIR are being built for the workflow healthcare is about to be required to run at scale.
What we've shipped in US healthcare
A healthcare AI agent that demos well is not the same system that survives a real clinic. MIT found that 95% of enterprise generative AI pilots deliver no measurable profit (MIT, The GenAI Divide, 2025). The gap is rarely the model. It is the data, the compliance, and the integration work that a demo skips.
We build on the side of that gap where the work actually runs. RaftLabs has shipped production software since 2015 for clients across the US, UK, Europe, Canada, and the UAE. The healthcare proof below is separate and specific: HIPAA-compliant systems we built and still support for US providers.
Proof
Healthcare systems we've shipped and still support
- less clinical decision time after the AI layer shipped
- 20%
- PDC remote patient monitoring, US
- HIPAA compliance held through the AI integration
- 100%
- PDC RPM platform
- patients enrolled in the first 12 weeks
- 150+
- PDC RPM launch
- clinics onboarded in the first 12 weeks
- 50+
- Galen nurse-assisted telehealth, US
Have a healthcare AI agent project?
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.
How it works
From scope to live agent
- Week 101
Workflow and EHR scoping
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.
- Weeks 2-302
Compliance architecture and checkpoint design
BAA coverage, data flow, and human-in-the-loop checkpoints designed and documented before any PHI flows through the system.
- Weeks 4-1003
Build and clinical validation
Agent workflow built against LangGraph, tested against real EHR sandbox data, and reviewed by your clinical team before go-live.
- Final 2 weeks04
Launch and monitoring
Production deployment with audit logging active from day one and escalation queues staffed and ready.
Pitfalls we plan around
An AI agent in a clinic fails in specific, predictable ways. We design against each one before it can reach a patient record.
- Hallucination in a clinical context
- An agent that invents a dosage, a code, or a criterion is worse than no agent. Clinical outputs are grounded in retrieval against approved sources, validated against a JSON Schema, and never finalised without a clinician. The agent assembles context; it does not diagnose.
- PHI leakage
- PHI never reaches an LLM without a signed BAA, moves over TLS 1.2 or higher, rests under AES-256, and logs every access event per 45 CFR 164.312. Retrieval stores sit inside a HIPAA-eligible, network-isolated environment with minimum-necessary access by design.
- Over-automation
- The real risk is an agent that acts where a human should decide. Every clinical decision, note signature, and triage redirect keeps a mandatory human-in-the-loop checkpoint. Routine volume is automated; clinical judgment stays with your staff.
What clients say
What our clients say
Three-year average engagement. Founders and operators describing the work in their own words. No marketing varnish.
Charles E.
USAEntrepreneur at Aggie Technologies
All of the sprints were completed on schedule and on budget. We highly recommend RaftLabs!
Proof
Case studies
Where you land depends on scope, not negotiation:
- Focused agent, $35,000-$75,000
- One workflow, one EHR integration, and HIPAA-compliant architecture, delivered in 10-14 weeks.
- Multi-agent system, $75,000-$175,000
- Prior auth, clinical documentation, and care gap outreach with FHIR R4 write-back and a vector database for clinical knowledge retrieval.
What it costs
Healthcare agents, starting at $35,000.
A focused agent covering one workflow, one EHR integration, and HIPAA-aware architecture, live in 10-14 weeks. Multi-agent systems add documentation, outreach, and FHIR R4 write-back.
We don't estimate EHR integration generically. API coverage varies materially between Epic, Cerner, and Athenahealth, so we confirm it during discovery. Most systems start with a single-workflow agent, then expand to a multi-agent system once the EHR connection is proven in production.
Starting investment
Starts at $35,000
Live in 10-14 weeks. Start with one workflow and one EHR integration, the highest-variance piece, scoped in detail before anything is built, then add agents once the first one proves out.
No hourly billing
Once we scope your first agent, that price is locked in writing, so there's no hourly billing and no invoice surprises. A scope change is a priced change request, agreed before work begins.
Human-in-the-loop
Every clinical decision, note finalisation, and triage redirect keeps a mandatory checkpoint. The agent assembles the context; a clinician makes the call.
Useful next steps
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Read moreFrequently asked questions
A 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
Tell us where the work is stuck.
Bring the rough workflow, half-built product, or messy brief. We will map the smallest useful first move, then send scope, timeline, and price in plain English.
- Scope and cost agreed before work starts. No surprises. No obligation.
- Working prototype within 3 weeks of kickoff.
- Pay by milestone. You see progress before each invoice.
- 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
- All conversations are NDA-protected.