How to Evaluate Healthcare AI Vendors: Ask for Evidence at Every Step

Evaluate healthcare AI vendors by matching each important claim to evidence from the workflow you intend to buy. Define the task, its permitted actions, the systems involved, and the result staff must verify. Then test normal cases, failures, and recovery. A convincing conversation is useful; an auditable result in the relevant environment is stronger evidence for a purchase.

Define what the purchase must accomplish

Start with a sentence a coordinator and a finance leader can both understand: "When an appointment request arrives, create a verified scheduling outcome or assign a traceable exception to the correct team." Replace the example with your actual task. Avoid starting with "We need agents," because a technology category does not establish the result you are buying.

Name the entry event, eligible population, exit condition, system of record, and accountable owner. An intake tool may solve a narrow problem well even if it does not manage an entire referral. A broader platform may fit if several handoffs are the source of the work. Breadth should follow the process, not determine it.

Use the operational AI overview to frame the administrative scope. If the proposed purchase crosses into clinical decisions, involve the appropriate clinical leadership and evaluate that scope separately. The distinction between operational and clinical AI belongs near the start of a buying discussion.

List the decisions the system may execute and those it may only prepare for staff. A vendor should be able to show how those boundaries are configured, tested, and changed. A statement that "humans stay in control" is incomplete until someone can identify the specific control.

Use a claim-to-evidence worksheet

The following worksheet is an original procurement tool. It is not a certification or a published industry scoring standard.

Add a date, evidence location, reviewer, and open question to every row. A slide can establish what the vendor says. It cannot establish that the configuration will work in your environment. Similarly, a successful demonstration supports the demonstrated case; it does not prove how often that case occurs in your workload.

NIST describes its AI Risk Management Framework as voluntary guidance for evaluating and managing AI-related risks. Use your organization's governance process alongside the procurement worksheet. Do not interpret the worksheet or a vendor's reference to NIST as independent approval of the product.

Move evidence through four levels

Documented capability. The vendor identifies what is supported and what is excluded. Capture the product version and dependencies. "Available on the roadmap" belongs in a separate column from "available for this purchase."

Observed demonstration. A person from your team watches the capability run. Record the starting conditions, inputs, result, and any manual preparation. Synthetic cases are appropriate here. A customer environment is not necessary for every demonstration, and access to customer data should never be treated as a prerequisite for credibility.

Local acceptance test. Your team runs agreed cases in an authorized environment representing your integration and configuration. This is where assumptions about field mappings, duplicate records, site rules, and staff permissions become visible.

Measured operating result. After a controlled deployment, compare observed outcomes with the agreed baseline and observation period. Keep denominator changes and human rework visible. A promising result in one workflow supports that workflow, not every feature in the sales presentation.

For booking-specific tests, use the voice AI and EHR integration checklist. For a referral procurement document, the referral management RFP template provides a narrower next step. This article's job is to help judge evidence across vendors, rather than repeat those detailed questionnaires.

Ask to see the difficult middle of a workflow

A happy-path demonstration usually starts with usable information and ends with a completed action. Ask what happens between those points when the next step is uncertain.

Try a duplicate request, an unavailable destination system, a conflicting update, and an ambiguous instruction. The vendor should show what is retained, what is retried, and what a person sees. Ask whether a retry could create a second appointment or duplicate task. An exception labelled "failed" without an owner merely describes unfinished work.

A useful staff handoff includes the original request, actions already attempted, current state, unresolved question, and next responsible team. It should also make clear when the automation has stopped. Staff need to know whether opening the case will race against another automated attempt.

Keep pass conditions separate from weighted preferences

Some requirements are necessary for the intended purchase. Others are preferences that can be traded against price or convenience. Do not let a polished interface compensate mathematically for an unverified required transaction.

Use a two-stage decision. First, identify mandatory acceptance conditions with the responsible reviewers. These may include the ability to recover failed bookings, provide the required administrative controls, or export your operational records. Then score preferences among vendors that have met those conditions, or clearly identify a limited pilot intended to resolve an outstanding condition.

Hypothetical example: a committee assigns 40 points to workflow fit, 30 to integration, 20 to staff usability, and 10 to commercial fit. Vendor A scores 88; Vendor B scores 82. However, Vendor A has not demonstrated recovery after a booking timeout, a mandatory condition. The correct result is "A remains conditional," not "A wins by six points."

The weighting is illustrative. Your priorities should reflect the actual job. Record why a criterion matters, who judged it, and what would change the judgment. This makes the decision understandable when a different manager inherits the implementation.

Check the business case without demanding a promise

Ask how the vendor calculated an outcome, not whether the vendor will guarantee yours. A cautious forecast can be more useful than an unsupported commitment.

Request the baseline population, measurement period, eligible workload, exclusions, implementation effort, and remaining staff responsibilities. If the organization plans to use released time for unresolved referrals, that is a capacity decision; it becomes a cash saving only if spending changes.

The referral automation ROI calculator framework shows how to keep benefits distinct. When evaluating phone work, use a phone-specific model rather than applying an end-to-end referral saving to every call.

Ask reference customers questions the presentation cannot answer: What required more work than expected? Which cases still go to staff? Who maintains configuration? What was excluded from the reported result? References can illuminate operating conditions without proving that the same outcome will occur for your team.

Write the decision while the evidence is fresh

Create a short decision record with the selected scope, alternatives considered, decisive evidence, conditions still open, expected recurring work, and named acceptance owners. Add the reason for rejecting each alternative without speculating about the vendor's motives or business stability.

Commercial review should cover the service you intend to receive, the units you will be charged for, support responsibilities, change requests, and the process for leaving. Send legal, privacy, and security questions to the designated reviewers. An operations checklist should gather their decisions, not substitute for them.

Finally, describe the condition under which you would stop or narrow the project. An exit condition is practical procurement discipline: it prevents an inconclusive pilot from becoming a permanent workflow nobody intended to buy.

If you are comparing Linear Health, bring the same evidence worksheet to a demo. A useful evaluation should leave your team with a clearer decision, including when the proposed scope does not fit.

FAQ

Sources

• National Institute of Standards and Technology (NIST), AI Risk Management Framework, voluntary AI risk-management context, nist.gov/itl/ai-risk-management-framework. The procurement worksheet and example in this article are original editorial tools.