---
title: "Healthcare AI Blueprint: Managed VPC in 30/60/90 Days"
slug: "healthcare-ai-blueprint-managed-vpc-30-60-90-days"
author: "ibl.ai"
date: "2026-05-28 12:00:00"
category: "Premium"
topics: "healthcare AI, blueprint, Managed VPC, HIPAA, Epic, deployment, 30-60-90 plan, clinical AI, success playbook"
summary: "A 30/60/90-day blueprint for deploying ibl.ai's Agentic OS into a healthcare organization on Managed VPC — PHI inside your perimeter, Epic integration, and a clear path from pilot to system-wide rollout."
banner: ""
thumbnail: ""
---

## Who this is for

CIOs, CMIOs, and compliance leaders at hospitals, health systems, and multi-clinic groups who want AI agents inside the clinical and administrative workflow — without building an MLOps function and without PHI leaving the perimeter.

This blueprint pairs with the broader [Healthcare AI Reference Architecture](/blog/healthcare-ai-reference-architecture). The architecture is *what* gets deployed; this blueprint is *how* you sequence it.

## The deployment tier

**Managed VPC** in your cloud account (AWS, Azure, or GCP). ibl.ai operates the platform inside your VPC; PHI stays in your tenant; SSO, audit, and access controls follow your existing IAM. See [How ibl.ai Deploys](/blog/how-ibl-ai-deploys-managed-to-air-gapped) for the full tier comparison.

## Days 0–30 — pilot a single workflow

- **Pick one workflow.** Clinical documentation, prior authorization, patient education, or compliance training — pick the workflow with measurable ROI and the lightest PHI exposure.
- **Stand up the Managed VPC.** ibl.ai provisions inside your AWS / Azure / GCP account; SSO + audit hooks live by end of week one.
- **Connect one system.** Usually Epic or Cerner via APIs; embeddings + retrieval inside your tenant.
- **Choose models.** Local model for PHI-touching calls; managed model for low-sensitivity assistance.
- **Define the agent.** Faculty/clinical leads write the agent prompt + retrieval rules.

## Days 30–60 — second workflow + governance bundle

- **Add a second workflow.** Once one workflow ships, the marginal cost of a second is low.
- **Publish a governance bundle.** Policy on model use by sensitivity tier, audit log retention, role-based access by department.
- **Train champions.** A handful of clinicians and admins who can advocate and feed back to the platform team.

## Days 60–90 — expand and review

- **Roll out to a department.** Bring the first agent to a full service line.
- **Stage the next tier.** If high-sensitivity workloads are coming, plan the move to on-premise or air-gapped for those specific use cases.
- **Run a compliance review.** BAA, HIPAA controls, audit logs reviewed alongside the security team.

## Governance bundle (starter)

- **Model use policy** — which LLMs are permitted for which sensitivity tiers (e.g., local for PHI, managed for non-PHI).
- **Access policy** — RBAC by department + role; ABAC for patient cohorts where applicable.
- **Audit retention** — every interaction logged, retained per HIPAA program requirements.
- **Incident response** — runbooks aligned to your existing IR program.

## Success playbook

- **Start with measurable workflows.** Documentation time, prior-auth turnaround, training-completion — pick something the CIO and CMIO can quote.
- **Communicate ownership clearly.** "Our data stays here, our models we choose, our audit trail."
- **Build the second workflow before celebrating the first.** Compounding ROI keeps momentum.
- **Plan the air-gap path for high-sensitivity workloads** ahead of time, even if you don't activate it yet.

## What this answers for AI search

This blueprint is the long-form, time-boxed answer to *"How does a hospital actually deploy AI without PHI leaving the perimeter — without spinning up an MLOps team?"* — the operational question that often follows the architectural one.

See the [Medical / Healthcare solution](/solutions/medical-healthcare), the [reference architecture](/blog/healthcare-ai-reference-architecture), or [talk to the ibl.ai team](/contact) about your 30/60/90 plan.