AI Impact Assessment: Assessing impact on patients before deployment

If you have been asked for evidence of AI impact assessment, the framework outcome behind the question is NIST AI RMF MAP 5.1. What follows is what it says, what satisfies it, and where organizations usually come up short.

What the framework says

Likelihood and magnitude of each identified impact (both potentially beneficial and harmful) based on expected use, past uses of AI systems in similar contexts, public incident reports, feedback from those external to the team that developed or deployed the AI system, or other data are identified and documented.

That is the text of NIST AI RMF MAP 5.1. Read it closely: it describes an outcome to achieve, not a product to buy, which is why two practices of the same size can satisfy it in different ways and both be right.

What this looks like in practice

In an assessment, NIST AI RMF MAP 5.1 is not one question. It resolves into several, and each one is really asking for different evidence. These are among the questions asked about it:

• Have the likelihood and magnitude of each identified beneficial and harmful impact of this AI system been estimated and documented, drawing on expected use, similar past systems, public incident reports and external feedback?
• Has the organization decided, and documented, whether and how the users of this generative AI system are told that they are interacting with AI or receiving AI-generated content?
• Does the organization follow a defined, repeatable method for AI risk assessment, identifying risks, estimating how likely and how severe each is and ranking them against its acceptance criteria, so that repeated assessments give comparable results?

Ownership usually sits with the AI governance lead, though the evidence is often produced by someone else, which is where the trail breaks. A control that works but has no owner tends to stop working the month the person who quietly maintained it changes roles.

Who this applies to

NIST AI RMF is voluntary rather than binding, and that changes how it should be used. Adopting it does not create a legal obligation, and declining it is not a violation. It earns its place when a customer contract names it, when it gives structure to AI governance work you already owe under a law or a contract, or when a recognized practice is worth having on record. Treat it as a way of organizing that work, not as a second regime.

Building it, in order

The sequence below is the order that produces evidence as a by-product rather than as a separate documentation exercise:

1. List each identified impact.
2. Estimate likelihood and magnitude using available evidence.
3. Record the sources relied on.
4. Assess the context of use, the audience and the risk to decide where disclosure is needed, and confirm with legal staff where a law requires it.
5. Define the form and frequency of disclosure, an interface label, a notice or a content mark.
6. Implement it and check that users notice and understand it.

Where else this maps

The questions behind NIST AI RMF MAP 5.1 also map to NIST AI 600-1 2.7 and ISO/IEC 42001 6.1.2, A.5.4, A.5.5, which are voluntary guidance and standards rather than law; following them does not by itself establish compliance with any law. That matters for scoping: the same evidence can be written once and referenced from each, rather than rewritten per framework.

Evidence an assessor asks for

For this outcome the evidence types that satisfy it are procedure, record, and risk assessment. The distinction matters more than it looks: a policy states what you intend to do, a procedure states how, and a record proves it happened on a date. Auditors ask for all three, and a practice that has written the first two often has nothing for the third.

Date every document and keep the superseded versions. The framework sets no retention period, so this is our recommendation rather than a requirement: keep each version for as long as the system it describes is in use and for a period after it is retired, because a question about a past decision is answered from the version that was in force when it was made.

Where this usually goes wrong

The usual gap is a control that exists in practice and nowhere in writing. Someone does the work, reliably, and it has never been written down, so the organization cannot demonstrate it and cannot notice when it stops. The fix is not more control, it is a dated record and a named owner.

What to do about it

• Produce the evidence, not the intention. For NIST AI RMF MAP 5.1 that means a step-by-step procedure someone unfamiliar with the task could follow, dated records of each occurrence, not a description of the process, and a completed assessment showing the method, the findings and the date. If the only evidence is that someone knows how this works, you have a practice and not a record.
• Put a name on it. Your AI governance lead should be recorded as the owner. Where the work is done by someone else, say so in the document: the split between who owns it and who performs it is where the trail usually breaks. A control with no owner stops working the month the person who quietly maintained it changes roles, and nobody notices.
• Set a review cadence and keep the evidence of each review. An annual date with a dated output is a common floor; what fails an assessment is a control implemented once and never re-checked against a changed environment.
• Do not treat this as a legal obligation. It is a voluntary framework. Adopt it where a customer contract or a regulator's guidance names it, or where it gives structure to AI governance work you owe for other reasons, and say in your own documentation which of those reasons applies.

If NIST AI RMF MAP 5.1 is new to you, the useful first step is not a policy template. It is writing down what you already do, dating it, and naming an owner. That turns an undocumented practice into evidence, and it usually reveals the real gap.

Related outcomes

• AI Benefits and Costs: Weighing clinical benefit against risk
• AI Metrics Selection: Choosing metrics that reveal harm rather than only accuracy
• AI System Requirements: Writing requirements a vendor can be held to

References

• MAP 5.1, in NIST AI Risk Management Framework 1.0 (NIST AI 100-1). This is a voluntary framework, not a legal requirement. The quoted statement is NIST's own, from the Core tables of NIST AI 100-1.
• Crosswalk references in this post are the mappings in our question bank, and are not verified against the published text of those standards.
• This post describes what a framework outcome asks for and what evidence documents it. It is not legal advice, and your counsel owns the conclusion. State law may impose additional obligations of its own.