HIPAA-compliant agents for clinical documentation, prior authorization, patient intake, and revenue cycle automation. Built for your EHR stack, not a generic one.
Off-the-shelf healthcare AI is built for the median clinical environment. If your workflows are specialty-specific, your EHR stack is non-standard, or your data is regulated, that median is not you.
Complex, highly specific clinical and enterprise healthcare workflows
Standard use cases with minimal clinical customization needs
Deep integration with Epic, Cerner, Athenahealth, and proprietary clinical systems
Limited or standardized EHR integrations only
Required when PHI must remain within your environment and bypass third-party inference layers
Suitable when data is non-sensitive and HIPAA constraints are minimal
Built-in HIPAA, SOC 2, HL7, and FHIR alignment from architecture stage
Relies on vendor compliance posture; limited configurability
Tailored to your specialty-specific protocols, documentation standards, and payer requirements
Designed for generic use cases; limited specialty adaptation
Full ownership of models, prompts, integrations, and data - no vendor lock-in
Dependent on vendor roadmap, pricing, and feature availability
Clinical staff spend more time documenting and routing than treating. The agents below target the workflows where that time goes.
The agent listens, structures the conversation, and writes the SOAP note. Documentation time drops 50–70% per visit. Physicians get that time back in the room.
Pulls criteria, matches payer guidelines, drafts the auth, and tracks submission. 3–5 day turnarounds resolve same-day.
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Collects structured history, flags high-acuity indicators, and routes to the right pathway before the appointment. The clinician walks in with context.
Specialty changes the agent. Cardiology handles multi-system encounters and device data. Behavioral health runs under stricter PHI rules and different note structures. Scoped to the specialty, not a template.
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High-volume encounter documentation, care gap identification, and triage automation at scale.
Multi-system workflows with protocol adherence requirements and complex clinical evidence retrieval.
Session notes, treatment plan documentation, and risk assessment under strict PHI handling and audit controls.
One agent, one workflow. Healthcare runs many. The build hands off across documentation, coding, billing, and scheduling without a human in between.
Connect with AI Experts Today!The orchestrator routes each task to the right specialist: documentation, coding, eligibility, scheduling. It handles exceptions, conflicts, and escalation.
Persistent memory carries patient context across encounters. The agent knows last visit's discussion, decisions made, and the current care plan, no full-chart re-pull.
Agents call tools, not just generate text. FHIR reads and writes, payer submissions, lab ingestion, scheduling access. The agent does the work.
High-stakes and low-confidence outputs route to a reviewer with full context: reasoning, sources, proposed action. Decisions feed back into the eval set.
An EHR integration that works in a sandbox and fails in production is a demo. We scope authentication edge cases, data normalization, and error recovery from day one.
Talk to Our AI ExpertsOur AI healthcare solutions integrate with leading EHR/EMR platforms including Epic and Cerner, enabling real-time data access without disrupting your existing infrastructure.
Eligibility verification, prior authorization submission, and claims status tracking through Change Healthcare, Availity, and direct payer EDI connections. Real-time where the payer supports it, batched with SLA monitoring where they don't.
Production integrations with Salesforce Health Cloud, Kareo, AdvancedMD, and leading RCM platforms. We scope the integration layer during discovery, no surprise dependencies surfaced after build begins.
Data from Apple Health, Fitbit, Dexcom, iHealth, and medical-grade monitoring devices feeds directly into RPM agents. The agent applies patient-specific thresholds, not population averages, when evaluating readings.
Stakeholder interviews, clinical workflow audits, and data inventories run in parallel. We leave this step with defined success criteria, measurable KPIs, integration requirements, and a ranked list of automation targets. Architecture decisions wait until we have this.
Audit existing clinical data sources, map PHI flows, establish BAA requirements, and build ingestion and de-identification pipelines. The agent gets reliable, compliant data. Problems discovered here cost hours to fix; problems discovered in production cost months.
Design agent architecture, select LLMs and frameworks, build RAG pipelines on your clinical knowledge bases, and run structured evaluation frameworks. We red-team the agent against clinical accuracy and hallucination scenarios before it touches a real workflow.
Build and validate EHR, payer, and lab integrations in a controlled pilot environment with real clinical workflows. Human-in-the-loop oversight runs throughout. Production rollout follows a go/no-go review with data from the pilot.
Post-deployment dashboards track performance, clinical accuracy, and workflow impact. Drift detection flags degradation before it reaches users. Monthly eval runs and retrieval optimization are part of the retainer, not extras.
Ranges come from production deployments. Outcomes shift with baseline, complexity, and adoption. KPIs are set at kickoff.
Start Your AI Agent Project Now!Ambient scribing cuts per-encounter documentation by 50–70%. For a 20-patient day, that returns 1–2 hours.
Automated prior authorization cuts turnaround from 3–5 days to same-day or next-day. Staff hours drop to near zero.
AI-assisted coding cuts errors by 30–50%. First-pass denials fall. Claims come back for rework less often.
Automating intake, eligibility, scheduling, and documentation cuts cost per encounter by 25–40% within the first full quarter.
Production AI in clinical documentation, RCM, and patient engagement since 2017. We know what breaks, because we have shipped through all of it.
Delivered production AI for Fortune 500 health systems and digital health companies. Enterprise procurement and security reviews are familiar territory.
AI engineers, MLOps, and compliance architects assigned to your project. The team that scopes the work builds it.
Book a 45-minute scoping call with a Folio3 healthcare AI architect. We will review your target workflow, your EHR environment, your compliance requirements, and your timeline and give you a straight answer on what is buildable, what it will cost, and what the fastest path to production looks like.
Start Your Healthcare AI Agent Project
Real-time payer API checks at scheduling: coverage, deductible, co-pay, and out-of-network flags. Front-desk staff stop calling for what an API returns in seconds.
Reads the clinical note and suggests ICD-10, CPT, and HCC codes with citations back to documentation. Coders review instead of coding from scratch. Denial rates drop 30–50%.
Conversational scheduling across SMS, portals, and email handles booking, rescheduling, and reminders. No-show rates fall 20–35% without added headcount.
Ingests data from CGMs, blood pressure cuffs, and wearables, compares against patient-specific thresholds, and alerts care teams when intervention is warranted.
Pulls from patient history, labs, imaging, and clinical literature simultaneously. Surfaces differentials and treatment options during the encounter, not hours later.
Procedure documentation, implant tracking, and post-operative care coordination.
Image-linked documentation, biopsy result tracking, and patient follow-up automation.
If the workflow, integrations, and compliance scope are defined, the agent can be built for it.
Every engagement targets a production-ready agent with integration complete and monitoring live. The PoC is a gate, not the deliverable.
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