Hire an AI Consultant · Nashville, TN

Hire an AI consultant in Nashville.

If you are a CMIO or CMO at HCA Healthcare, Community Health Systems, or Vanderbilt UMC, you are receiving vendor pitches for clinical AI every week. Epic AI features, ambient documentation tools, clinical decision support platforms. Each one comes with accuracy claims and a reference customer list.

Evaluating these requires two things most vendors know you do not have time to do: independent validation of their accuracy claims against your patient population, and a clear read on whether the FDA regulatory pathway is actually settled.

An independent AI consultant evaluates the claims, reviews the validation data, maps the FDA SaMD classification, and tells you whether the clinical workflow integration is realistic before you sign anything. Nashville's logistics sector faces a parallel problem: Genco and Ingram Industries receive warehouse AI pitches with vendor-supplied ROI projections that do not reflect their specific SKU mix or facility constraints.

Fixed scope, fixed price, agreed upfront depending on scope and the number of vendors evaluated.

How we work

Tell us about your AI vendor evaluation.

Evaluating clinical AI vendor claims.

Vendor accuracy benchmarks are not lies. They are just measured on datasets that do not look like your patient population.

Validation data review

We request the vendor's validation dataset composition: patient demographics, institution count, label methodology, and time range. A model validated on academic medical center data may perform differently at a community hospital with a different case mix. We ask for subgroup-stratified results, not aggregate accuracy. That one request surfaces performance gaps vendors rarely volunteer.

Proof-of-concept test design

For vendors that pass the documentation review, we design a prospective or retrospective POC test against a representative sample of your own data. The test protocol specifies the evaluation criteria, the ground truth methodology, and the minimum performance threshold required to recommend proceeding. You see the methodology before the test runs, not after.

Physician workflow fit assessment

Alert fatigue is the primary reason clinical AI tools get ignored after deployment. We interview physicians who represent the actual user population, not champions selected by the vendor. The questions focus on current documentation workflow, existing alert burden, and previous tools that were abandoned. Adoption risk shows up in those interviews.

Contract risk identification

Clinical AI contracts often contain performance SLAs tied to vendor benchmarks rather than your data, data use provisions that allow model retraining on your patient records, and lock-in terms that are expensive to exit. We flag these before the contract goes to legal, not after.

The FDA SaMD pathway.

Most clinical AI vendors tell you their product is not a medical device. Sometimes that is correct. Sometimes it is a regulatory position that has not been tested.

Administrative vs. clinical function

Tools that automate documentation (generating SOAP notes, coding diagnoses from transcripts) are generally administrative under the CURES Act. FDA has not asserted device jurisdiction over these. Tools that surface clinical recommendations change the analysis.

When 510(k) clearance applies

A clinical decision support tool that is intended to diagnose, treat, or prevent a disease is SaMD. Class II designation and 510(k) clearance apply to many AI diagnostic aids. The vendor's regulatory status document should specify their predicate device and clearance date. If they cannot produce it, the pathway is unresolved.

What this means for procurement

An FDA-cleared tool has gone through a defined validation process. A non-cleared tool may be performing equally well or poorly, the absence of clearance is not a quality signal. What it is: a liability allocation signal. Your health system's legal team needs the regulatory status before the contract closes.

How we work with Nashville clients.

Discovery call, then written scope

We start with a 45-minute call to understand which vendors are in consideration, what your evaluation timeline is, and what a good outcome looks like. You receive a written scope document and fixed price before any work starts. No hourly billing.

Remote-first, async by default

Shared Slack channel, written updates every Friday, recorded walkthroughs for every deliverable. Clinical leaders at large health systems do not have time for weekly status calls. We work on your schedule.

Two-week start

We can start within two weeks of a signed agreement. Useful when a vendor has set a proposal expiration date and you need an independent evaluation before it lapses.

Questions we get from Nashville clients.

How do you independently verify a clinical AI vendor's accuracy claims. What validation dataset do you ask to see?: We ask for three things: (1) the exact dataset composition: patient demographics, institution count, time range, and how labels were generated; (2) performance stratified by subgroup, not just overall accuracy, because a model that is 93% accurate overall may be 74% accurate on the specific patient population your health system serves; (3) prospective validation results if they exist. Most vendors have only retrospective validation from curated datasets. That is not sufficient to predict real-world performance in your EHR environment. If a vendor cannot produce stratified validation data, that is material information for the purchase decision.
What FDA SaMD classification applies to an ambient clinical documentation tool, does it require clearance?: Most ambient documentation tools (automated SOAP note generation, AI scribe) are designed to reduce physician documentation time, not to inform clinical diagnosis. FDA has generally classified these as non-device software under the CURES Act because they are administrative in function. However, if the tool surfaces clinical suggestions (flagging a missed diagnosis, recommending a medication) the classification changes. Class II or Class III SaMD designation becomes possible, and 510(k) clearance or De Novo authorization may be required. The vendor's FDA regulatory status document should specify their classification and the basis for it. If they cannot produce that document, the regulatory pathway is unresolved.
How do you assess physician adoption risk for a new clinical AI tool before the purchase decision?: Adoption risk is rarely about the AI's accuracy. It is about workflow fit. We conduct structured interviews with 6-10 physicians who represent the intended user population, not physician champions selected by the vendor. The questions cover documentation workflow, alert fatigue patterns, and what tools they have abandoned before. We also review the tool's alert design against published alert fatigue research. HCA Healthcare's internal studies on alert fatigue thresholds are useful benchmarks for health systems evaluating ambient documentation tools. If physician interviews surface consistent friction with the proposed workflow, that friction predicts low adoption regardless of technical performance.
What should a Nashville health system's AI governance policy cover, what's actually required vs. nice to have?: Required: a documented process for validating AI model performance on the health system's own patient population before deployment, not just vendor-supplied benchmarks; a bias monitoring protocol with defined subgroups and review cadence; a human oversight requirement for any AI output that could affect a clinical decision; and a clear policy for when a model must be retrained or taken offline. Nice to have but not required: a formal AI ethics committee, publication of model cards, and a public transparency report. The Joint Commission does not yet have specific AI governance standards, but CMS's evolving Conditions of Participation guidance is moving in that direction. Governance policies written now should anticipate that direction.

Get an independent read before you sign.

Tell us which vendors are in consideration and what your decision timeline looks like. We reply within one business day with a rough scope and price range.

Hire an AI consultant in Nashville.

Fixed scope, fixed price, agreed upfront depending on scope and the number of vendors evaluated.

Evaluating clinical AI vendor claims.

Vendor accuracy benchmarks are not lies. They are just measured on datasets that do not look like your patient population.

Validation data review

Proof-of-concept test design

Physician workflow fit assessment

Contract risk identification

The FDA SaMD pathway.

Most clinical AI vendors tell you their product is not a medical device. Sometimes that is correct. Sometimes it is a regulatory position that has not been tested.

Administrative vs. clinical function

When 510(k) clearance applies

What this means for procurement

How we work with Nashville clients.

Discovery call, then written scope

Remote-first, async by default

Two-week start

We can start within two weeks of a signed agreement. Useful when a vendor has set a proposal expiration date and you need an independent evaluation before it lapses.

Questions we get from Nashville clients.

How do you independently verify a clinical AI vendor's accuracy claims. What validation dataset do you ask to see?

We ask for three things: (1) the exact dataset composition: patient demographics, institution count, time range, and how labels were generated; (2) performance stratified by subgroup, not just overall accuracy, because a model that is 93% accurate overall may be 74% accurate on the specific patient population your health system serves; (3) prospective validation results if they exist. Most vendors have only retrospective validation from curated datasets. That is not sufficient to predict real-world performance in your EHR environment. If a vendor cannot produce stratified validation data, that is material information for the purchase decision.

What FDA SaMD classification applies to an ambient clinical documentation tool, does it require clearance?

Most ambient documentation tools (automated SOAP note generation, AI scribe) are designed to reduce physician documentation time, not to inform clinical diagnosis. FDA has generally classified these as non-device software under the CURES Act because they are administrative in function. However, if the tool surfaces clinical suggestions (flagging a missed diagnosis, recommending a medication) the classification changes. Class II or Class III SaMD designation becomes possible, and 510(k) clearance or De Novo authorization may be required. The vendor's FDA regulatory status document should specify their classification and the basis for it. If they cannot produce that document, the regulatory pathway is unresolved.

How do you assess physician adoption risk for a new clinical AI tool before the purchase decision?

Adoption risk is rarely about the AI's accuracy. It is about workflow fit. We conduct structured interviews with 6-10 physicians who represent the intended user population, not physician champions selected by the vendor. The questions cover documentation workflow, alert fatigue patterns, and what tools they have abandoned before. We also review the tool's alert design against published alert fatigue research. HCA Healthcare's internal studies on alert fatigue thresholds are useful benchmarks for health systems evaluating ambient documentation tools. If physician interviews surface consistent friction with the proposed workflow, that friction predicts low adoption regardless of technical performance.

What should a Nashville health system's AI governance policy cover, what's actually required vs. nice to have?

Required: a documented process for validating AI model performance on the health system's own patient population before deployment, not just vendor-supplied benchmarks; a bias monitoring protocol with defined subgroups and review cadence; a human oversight requirement for any AI output that could affect a clinical decision; and a clear policy for when a model must be retrained or taken offline. Nice to have but not required: a formal AI ethics committee, publication of model cards, and a public transparency report. The Joint Commission does not yet have specific AI governance standards, but CMS's evolving Conditions of Participation guidance is moving in that direction. Governance policies written now should anticipate that direction.

Get an independent read before you sign.

Tell us which vendors are in consideration and what your decision timeline looks like. We reply within one business day with a rough scope and price range.