Healthcare AI Consulting

Healthcare AI consulting can become very abstract very quickly.

Strategy decks. Maturity models. Transformation roadmaps. A quadrant explaining that data is important.

All true.

I prefer something more operational.

At the end of the work, somebody should know:

"This is the workflow. This is where AI helps. These are the systems and data it needs. These are the risks. This is what we should buy, what we should build, and this is the first version worth testing."

If the consulting does not get us closer to that sentence, it is probably too theoretical.

What healthcare AI consulting should actually produce

Depending on the engagement, the work can cover:

• AI opportunity discovery across a clinic or healthcare business;
• workflow mapping and automation design;
• technical feasibility;
• EHR and legacy-system integration strategy;
• data architecture and RAG;
• agent architecture and tool design;
• vendor/product assessment;
• build-vs-buy decisions;
• privacy, security and AI-risk design;
• evaluation and pilot design;
• product architecture for healthcare AI startups;
• implementation scope and development estimate.

Sometimes the output becomes a development project with us.

Sometimes it becomes a specification for your internal team.

Sometimes it becomes:

"Do not build this. Product X already solves 90% of it."

That is still a successful consulting result.

Finding the workflows worth automating

Most organizations start with ideas rather than evidence.

"We need an AI assistant."
"We should use agents."
"Can we automate the call center?"

I usually want to go one level lower.

Where is staff time going?

Which process creates queues?

What information gets copied between systems?

Which patient questions repeat every day?

Where do referrals get stuck?

What does a physician do before every visit that could be prepared automatically?

Which calls or chats does management wish it had time to review?

Then we score the workflow by volume, human effort, data availability, actionability, implementation complexity and risk.

That tends to produce much better projects than starting with whichever AI technology appeared on LinkedIn this week.

Architecture and feasibility review

Sometimes the problem is already known. The uncertainty is technical.

For example:

• Can we connect the agent to the EHR without replacing it?
• Can a model safely use this patient context?
• Should we use RAG, structured queries or both?
• Does FHIR expose enough of the workflow?
• Can the system run on-premise?
• Which actions should be deterministic?
• How do we prevent duplicate or unsafe transactions?
• How do we evaluate the agent before launch?

We can review an existing architecture or design one from the problem backward.

That includes the unglamorous questions: identity, logs, retries, permissions, source truth, failure recovery and what happens when an external vendor is unavailable.

Vendor selection and build-vs-buy

The healthcare AI market is large enough now that "build everything" is usually the wrong default.

We can help evaluate whether a product actually fits your workflow.

For an AI receptionist, for example, I would not stop at feature checkboxes. I would test:

• your actual language;
• your provider/service rules;
• your EHR integration;
• payer questions;
• identity handling;
• multi-turn context;
• tool failures;
• human escalation;
• what administrators can configure themselves.

For the general framework, see Build vs Buy Healthcare AI.

Safety, privacy and governance

AI governance becomes useful when it changes how systems are actually designed and used.

We can help define:

• which data can reach which AI services;
• which accounts/vendors are approved;
• where PHI is stored or logged;
• which workflows require human review;
• what patient-facing agents are allowed to answer;
• what actions require confirmation;
• how model and prompt changes are validated;
• how incidents are detected and reviewed.

This does not replace legal counsel. It turns legal and policy requirements into something engineering can actually build.

Useful starting points on this site are Healthcare AI Security & Compliance, LLMs + PHI and Healthcare AI Guardrails.

What you get at the end

I am not a fan of deliverables that exist only to prove consulting occurred.

Depending on the scope, useful outputs can include:

• current-state workflow map;
• prioritized AI/automation opportunities;
• target workflow;
• architecture and integration design;
• vendor comparison;
• risk and guardrail requirements;
• data/context design;
• evaluation plan;
• MVP scope;
• implementation phases;
• development estimate and assumptions.

The exact form depends on what you need next.

If your board needs a decision, the output should support a decision.

If developers need to build, the output should be technical enough to build from.

If you are choosing between vendors, it should make the trade-offs explicit.

Typical consulting engagements

One or two expert sessions. You have a concrete architecture or product question and want a second opinion.

Workflow discovery. We interview the people doing the work, inspect systems/data and identify where AI or ordinary automation has the best ROI.

AI architecture review. You already have a team or prototype and want to find the production risks before customers do.

Vendor assessment. We test products against the actual workflows rather than marketing claims.

MVP/product design. For healthcare founders who know the domain problem but need the technical product reduced to a buildable first version.

Pre-development discovery. The normal first phase before we build a custom healthcare AI system.

Who this is useful for

Consulting makes the most sense when the problem is expensive enough that guessing is a bad strategy.

That includes:

• clinics and healthcare groups choosing their first serious AI projects;
• health-tech companies designing an AI product or feature;
• organizations integrating AI into existing EHR/legacy systems;
• teams that already built a prototype and now need production architecture;
• investors/founders who need a technical reality check on a healthcare AI idea;
• companies deciding whether to buy a vendor product or build custom.

If the question is simple enough to answer in the first call, I will answer it in the first call.

The first conversation is free.

Paid consulting, analytics and data work start at $50/hour.

Healthcare is complicated enough. I see no reason to make the consulting commercial model mysterious too.