AI is either your engine's force multiplier or an expensive distraction. I make sure it is the first one.
The healthcare AI landscape is noise, hype, and solutions that would get you sued. I find the use cases that actually produce revenue, design them for production, and teach your team to run them in a regulated industry.
Noise versus reality
- "Autonomous AI that replaces your team"
- AI that returns time to your team, human in the loop, always.
- "Deploy across your org in 3 days"
- Real healthcare AI deployment is phased, tested, compliance-reviewed, and deliberate.
- "Our platform does everything"
- The best deployments nail 2-3 high-ROI use cases first, then expand from proven ground.
- "Fully automated decision-making"
- In healthcare, AI augments human judgment. It never replaces clinical decisions.
The discipline
How AI actually returns money in a regulated industry.
- use cases we prioritize first, scored on impact and risk, not guessed
- 2-3
- day reviews before anything expands to the next use case
- 30/60/90
- clinical decisions automated: AI augments, a human always decides
- 0
- of deployments reviewed for HIPAA and bias before they ship
- 100%
The build
- 1
AI readiness assessment
I assess your data maturity, tech stack, team capabilities, and regulatory constraints, together, in a workshop, not a one-way audit. You understand exactly where you stand before I touch a single tool.
- 2
Use-case prioritization
I workshop the opportunities and score them on revenue impact, feasibility, compliance risk, and time to value. You leave with 2-3 to pursue, not 47. Focus is how AI delivers ROI in healthcare.
- 3
Build and deploy
I design the specifics with your team in working sessions. Content automation. Predictive analytics. Patient communication flows. Workflow automation. Custom model deployment. I design it; your team builds and owns it as they go.
- 4
Govern and train
AI policies. Bias protocols. Human-in-the-loop procedures. HIPAA guardrails. Team training on prompt engineering and quality assurance. Safe, sustainable, auditable.
- 5
Measure and expand
30/60/90-day reviews. Refine based on real usage data. Expand to additional use cases only after the first ones deliver proven, measured ROI.
Use cases I design
The systems I design, and what each one produces.
Content automation at scale
Draft-to-publish pipelines for condition pages, provider bios, and local landing pages, with human clinical review built into the workflow. Weeks of compliant content in days, instead of a backlog that never ships.
CRM lead scoring and routing
Scoring and routing that runs inside your live CRM, putting the highest-intent inquiries in front of your best reps first, so a hot lead is never sitting in a queue while a competitor calls them back.
Predictive conversion modeling
Models that learn which leads, keywords, and campaigns actually become admissions, so spend and staff time follow the patterns that convert instead of the ones that merely click.
Patient communication flows
Automated, human-supervised follow-up across SMS, email, and voice that keeps families engaged between first contact and admission: the persistent nurture your team never has time to run manually.
Operational workflow automation
The repetitive back-office work (intake data entry, insurance checks, status reporting) handled by automation, so your team spends its hours on people, not forms.
Retention and churn signals
Early-warning models that flag the accounts and referral sources most likely to disengage, so your retention effort lands before the relationship is already gone.
My AI cred
I ship AI into production. Not into strategy decks.
I have built custom model pipelines for healthcare content, lead-scoring and routing that runs inside live CRMs, and automation wired across real operational workflows. I know what is real because I run it in production, in a regulated industry, which is exactly the lens I bring to your roadmap: what will actually work and survive a compliance review, not what demos well in a pitch.
Built with
Use-case prioritization
Not 47 AI pilots. The 2-3 that pay and stay compliant.
Every candidate scored on revenue impact against compliance risk. The top-left corner (high return, low regulatory exposure) is where we start. Everything else waits for proof.
Swipe to explore →
Works with
Where the intelligence gets deployed.
A roadmap is only worth the systems it ships into. These are the components AI plugs into to produce revenue.
- Capture layer
AI Voice & Chat
The most proven AI use case in healthcare marketing: production voice agents handling real patient calls.
Explore → - Connective tissue
Revenue Operations & CRM
Where lead scoring and routing models live: inside the CRM, acting on real pipeline.
Explore → - Demand generation
Growth Marketing
Content automation and predictive modeling that feed the demand engine.
Explore →
Cut through the AI noise. Find the 2-3 things worth building.
One workshop. I score your AI use cases on revenue impact, feasibility, and compliance risk, and you leave with the two or three worth building first, not a deck full of someday.