AI Implementation · Healthcare Technology · Practice to Enterprise

We build healthcare AI that earns its keep.

Practices, facilities and health systems are all adopting AI. Making it pay off inside one organization is the harder problem — and the gap is rarely the model. It's the EHR integration, the workflow nobody redesigned, and the person who has to use it between patients. That gap is the work.

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Background Meditech · Picis LumiraDx · Roche VYUhealth — Founder FHIR · HL7 · SaMD Spec-Driven AI Development Epic · Athena · eCW · NextGen

What We Build

What we build.

Four practices, built on twenty years inside healthcare technology, not advisory theory.

AI Implementation

Every practice is being called by someone selling AI. We find where it produces real ROI in your operation, then build and ship it instead of writing a deck about it, including an LLM and retrieval implementation that cut support contact volume by 40%. We're just as direct about where the ROI isn't there, which usually saves more than the pilots make. Where the environment is governed or regulated, we know which steps can safely be delegated and how to document that.

EHR and Platform Work

An EHR gets configured for how the practice worked on the day it went live. Since then the platform has shipped new versions and, lately, AI features many practices have never switched on. We review the configuration against how you run now, cut the documentation load that comes from the system rather than the medicine, and make sure someone inside can change a template without calling the vendor. Twenty years across Epic, athenahealth, eClinicalWorks, NextGen and Meditech, on both sides of the implementation.

Integration and Interoperability

Connections that used to take a project now ship as an API, and most practices still move records by fax and re-key the same information into two systems. We build the layer that removes those steps, in HL7, FHIR and IHE, and we take on the connection that's blocking a rollout from reaching production. We've delivered integration architecture behind $6M+ in recurring revenue.

Workflow and Operations

Platforms have added more in the last three years than most practices have had time to absorb, and the operating model rarely moves at the same speed. We follow the patient the whole way through, from how they get an appointment to how they check in, get seen, get billed and hear back. Then we fix the steps that cost your staff time and your patients goodwill, and document the ones worth doing the same way every time.

Also Available

Founder Advisory

Separately, we advise a small number of first-time founders on the operational questions that don't have obvious answers: governance, IP protection, cap table mechanics, the hiring sequence that actually works, and regulatory positioning before it becomes a crisis. It's a different engagement from the implementation work, and it's deliberately limited.

Agentic Development & Delivery

We build spec-first. The specification comes before the agents, the agents work under human review gates, and every decision leaves a trace someone can follow a year later. This is how we ship at the pace of a much larger team without giving up the traceability a serious buyer expects.

Method

How we build.

Most teams point an AI at a codebase and hope. We work in three steps — and the one most people skip is the one that decides whether any of it holds up.

Specify

The spec is the artifact. Requirements, constraints, acceptance criteria and regulatory boundaries are written down and agreed before an agent touches anything. Ambiguity resolved here costs a conversation; ambiguity resolved in production costs a finding.

Delegate

Agents do the work agents are good at — scaffolding, tests, migrations, refactors, documentation — inside boundaries the specification already set. Speed comes from the constraint, not from removing it.

Verify

Human review gates at every step that carries consequence, and a traceable record of what was decided, by whom, and why. When a regulator, a customer or your own engineering lead asks how the system got this way, there is an answer.

Twenty years of shipping software taught us where those gates belong. In practice this means spec-driven development frameworks — BMAD, OpenSpec, Spec Kit — adapted for environments with a quality management system attached. Retrieval and fine-tuning are table stakes now. The differentiator is knowing what an agent is allowed to decide on its own, and being able to show your work when someone asks. The frameworks will change. The discipline won't.

Who It's For

Four rooms.
The same gap in each one.

We take a small number of engagements at a time. The work is hands-on, not templated.

Ambulatory

Independent Practices & Small Groups

You don't have an IT department, and every AI vendor calling you sounds the same. You need someone who has actually implemented in your EHR and will tell you plainly what's worth buying.

You're being pitched ambient scribing, coding or intake AI and can't evaluate it
Your EHR is the real constraint on everything you want to do
You want a straight answer on ROI before you commit budget

Post-Acute

Skilled Nursing & Long-Term Care

Thin margins, heavy documentation and constant staffing pressure — the conditions where AI should help most, and where almost nobody credible is helping.

Charting and documentation load is eating clinical time you don't have
You need AI that works with the systems you already run, not a rip-and-replace
Ownership wants a business case, not another pilot

Enterprise

Health Systems & Provider Networks

You already have the pilots. Getting them past the pilot stage — through integration, security review and the operational change — is where they stop.

Pilots that work in one department and never scale past it
Integration and interoperability blocking the rollout
Nobody owns the workflow redesign the system actually requires

Founders

Healthcare IT & AI Startups

We've been on your side of the table and on the buyer's. Twenty years of knowing what health systems will actually buy, how implementation really goes, and what quietly kills a deal in security review.

You're building for healthcare without healthcare implementation depth on the team
You need product and regulatory positioning that survives a real buyer
You want an operator who has founded, sold into, and implemented in this market

Engagement

Three ways to engage.

Every engagement is senior-led. You work directly with the person doing the work, not a rotating bench of junior consultants.

Model 01

Fractional Retained

Starting at $8,000/month

An ongoing embedded role — typically 10–20 hours per month. Senior implementation and operating capacity without the full-time hire. Best for an active 6–18 month window where the work is real but a permanent role isn't yet justified.

Model 02

Project-Based

$5,000–$20,000+

Defined scope, defined deliverable, defined timeline. Examples: an AI implementation roadmap, a spec-driven build engagement, integration architecture, pilot design and delivery, or an AI governance framework. Priced by scope after an initial conversation.

Model 03

Founder Advisory

Starting at $4,000/month

The secondary practice. A light-touch retained engagement for founders who have operational capacity but need senior thinking on specific decisions. A few structured hours per month — calls, async reviews, decision support. Deliberately limited.

How We Start

The Working Session

Every engagement starts the same way — 60 to 90 minutes on your actual workflows, mapping where AI produces real ROI and where it won't. You leave with a one-page summary you can take to your partners or your board, whether or not you hire us. No cost, and no sales pitch.

About

Michael DiFazio

Founder, Palm Bay Labs · Healthcare Technology · AI Implementation

I've spent twenty years at the intersection of healthcare and technology — starting in hospital IT and building toward Director of Implementation and Customer Success at a global medical diagnostics company with $500M in annual revenue. Along the way I built integration partnerships that generated $6M in recurring revenue, led an AI implementation in a regulated clinical environment that cut contact volume by 40%, managed product launches across four continents, and oversaw teams in the US, Europe, India, and Latin America.

In 2025 I stepped out of the corporate track to found VYUhealth, a healthcare data intelligence company. I built it from scratch using AI development tools, signed letters of intent with health systems, and completed an IP assignment in 2026. That experience — blank page to working product to a completed exit — taught me things a career inside large organizations simply can't.

Palm Bay Labs is the practice built on that experience. We work with a small number of clients at a time: organizations that need AI actually implemented inside real constraints, and founders who need an operator who has been in the room. The work is specific, senior, and bounded. Healthcare is where we proved it, not where we stop.

40%

Contact reduction via AI implementation

20+

Years in healthcare technology

$500M

Revenue scale at LumiraDx

$6M+

Integration revenue generated