Healthcare AI Consulting: Picking an LLM

What should healthcare AI consulting settle before you pick an LLM?

Before choosing a model, settle the workflow, the data source, the risk tier and the success metric. A model chosen first tends to be justified afterwards, which is how pilots end up with impressive demos and no owner.

Healthcare AI consulting done properly starts with one narrow workflow: summarising discharge notes, drafting prior-authorisation letters, triaging inbound referrals, or answering internal policy questions. Each has a different tolerance for error. A wrong summary a nurse reviews is an annoyance; a wrong dosage suggestion shown to a patient is an incident.

Write down four things per workflow: who reads the output, what happens if it is wrong, which systems hold the source data, and what number would count as success. We ask for these in the first discovery call because every later decision, including model choice, follows from them.

How do the main LLM deployment options compare?

There are four realistic patterns: a hosted frontier API, a cloud-provider managed service, a self-hosted open-weight model, and a small specialised model. Each trades capability against control.

AWS maintains a list of HIPAA-eligible services, and the other major clouds publish equivalents. Eligibility is a precondition, not a guarantee: you still configure access, logging and retention yourself.

Why does the data matter more than the model?

In most healthcare deployments, retrieval quality and data plumbing set the accuracy ceiling. Swapping one strong model for another usually moves results less than fixing how records are extracted, de-duplicated and indexed.

Clinical data arrives as scanned PDFs, HL7 and FHIR feeds, free-text notes and spreadsheets maintained by one person. A model reading a badly OCR'd referral will fail regardless of its benchmark score. That is why our data engineering work often takes more of a pilot's calendar than prompt design, and why document AI for extraction is usually the first thing we build.

Where does business intelligence fit?

Healthcare business intelligence consulting and LLM work overlap more than vendors admit. A natural-language layer over a warehouse is only trustworthy if metric definitions, such as "readmission" or "length of stay", are already governed. If two departments define them differently, the model will confidently report both.

What about ERP and legacy systems?

Healthcare ERP consulting matters because supply, billing and scheduling data sit in systems with brittle interfaces. Plan for API wrappers or an integration layer rather than direct database reads. Our AI integration engagements usually spend the first two weeks on exactly this.

How do you handle PHI and compliance with an LLM?

Treat every prompt as a potential disclosure. Use a HIPAA-eligible service under a signed BAA, minimise what enters the prompt, and log access. The HHS Security Rule guidance defines the administrative, physical and technical safeguards you will be audited against.

In practice, build four controls:

1. De-identify or tokenise identifiers before text reaches the model, and re-attach them afterwards.
2. Restrict retrieval by the requesting user's permissions, not just the application's.
3. Store prompts and outputs in your own tenancy with a defined retention period.
4. Keep a human reviewer in the loop for anything that touches a clinical decision.

If the software could qualify as a medical device, the FDA's page on AI-enabled medical device software is where the regulatory conversation starts. Get regulatory counsel involved early; an engineer's reading of intended use is not enough.

Do pharma and life sciences need a different approach?

Pharma use cases differ mainly in the evidence standard: outputs must be traceable to a source document, and validation expectations are stricter. The model choices are similar, but audit trails and versioning carry more weight.

Healthcare pharma AI consulting typically centres on literature review, regulatory document drafting, pharmacovigilance case intake and trial-site matching. In each, the useful pattern is retrieval-augmented generation, introduced in the original RAG paper, where the model answers from retrieved passages and cites them. A reviewer can then verify the claim in seconds instead of trusting it.

Freeze model versions during validated workflows. A silent provider update that changes output style can invalidate a validation exercise, so pin the version and re-test deliberately when you upgrade.

How should you evaluate models on your own records?

Build a golden set of 100 to 300 real, de-identified cases with answers agreed by two clinicians or coders, then score every candidate model against it. Public benchmarks tell you about their test sets, not your documents.

Research such as Google's Med-PaLM work in Nature shows that models can reach strong scores on licensing-style questions. It also documents gaps against clinician answers, which is the point: exam performance and workflow performance are different measurements.

What should the golden set include?

Include the ugly cases: abbreviations, contradictory notes, missing fields and long records. Tag each case by difficulty so you can see where a cheaper model is good enough and where it is not.

Does long context solve retrieval?

Not reliably. The Lost in the Middle study found that models use information at the start and end of long inputs better than the middle. Test with your longest real charts, and prefer targeted retrieval over pasting an entire record.

What does a 30-day pilot look like?

A good pilot picks one workflow, one data source and one metric, then runs a week-by-week plan: access and data audit, retrieval and prompt build, evaluation against the golden set, and a supervised trial with real users.

This is the shape of our Moonship offering: a working MVP in 30 days against a fixed scope. The fixed scope is the discipline. It forces the argument about what is in and out to happen before build, and it keeps the pilot from absorbing every adjacent request.

Expect friction outside engineering. Security review, record access and clinician time are the usual blockers, and none of them respond to more developers. If you plan for them in week one, the 30 days holds.

When should you bring in a partner instead of building in-house?

Bring in a partner when you lack senior people who have shipped LLM systems under compliance constraints, or when a pilot needs to start before you can hire. Keep it in-house when you have that team and the workflow is core to your product.

Viithiisys has shipped software since 2007, with 500+ projects delivered for clients including Paytm, Snapdeal, IKEA, Nestle, Shiprocket and Vikram Solar. Our engineering sits in Mohali, in the Chandigarh tricity, with a Canadian office in Markham, Ontario, and we serve teams across the US, UK and Canada among six countries. Our AI consulting and LLM development teams work on a fixed-scope or fractional basis, scoped after a discovery call.

If you are unsure where the friction really is, start with a broken workflow assessment to find the process worth automating first. To discuss a specific healthcare use case, contact us.