What to ask when comparing ambient AI documentation tools

This article is part of a series exploring implementation lessons from Gold Coast Hospital and Health Service's 16-week evaluation of ambient documentation across 7,499 consultations. For the full analysis and all implementation lessons, see our complete article.

Look past demos to day-to-day reality

Ambient documentation is still a fast-moving category. When comparing vendors, it helps to look past demos and ask how the tool performs in day-to-day clinic reality, and how the vendor manages quality over time. Here are practical questions to ask:

1. Quality definitions and detection

How do you define and classify quality issues (capture problems, mis-structuring, hallucinations, bias)?

How are issues detected automatically and via user reporting?

Can clinicians flag an issue in seconds?

Why this matters: What looks like a "hallucination" can arise for different reasons. From gaps or ambiguity in what was captured, to missing context, to errors in attribution or synthesis. Vendors need structured approaches to classify and address different error types.

2. Review workflow

Does the interface make it easy to verify key details (medications, numbers, diagnoses, procedures)?

Are edits obvious, trackable, and fast, or easy to miss?

Why this matters: The goal isn't zero edits: it's making reviews faster, more reliable, and harder to miss. Good implementations make key details easy to check and corrections frictionless.

3. Monitoring and governance

Can you track quality trends by specialty, template, and model version?

How do you test updates before release?

What happens when an update makes something worse?

Why this matters: Quality can vary by specialty and template. Teams need visibility into trends over time to identify and address patterns before they become systemic issues.

4. Feedback loops

What happens after a clinician flags an issue?

How quickly do you respond?

What reporting do customers get, and how often?

Why this matters: It's imperative that ambient documentation tools make it easy for clinicians to flag concerns in the moment, and that teams have a structured quality assurance process to triage reports, investigate patterns, and minimise repeat issues over time. Closing that loop matters too - sharing outcomes with clinicians so they understand what happened, why it happened, and how similar issues will be handled in future.

5. Transparency and partnership

Will you share quality metrics and respond to independent evaluation?

What does support look like post go-live?

Why this matters: In practice, this is a shared responsibility: healthcare teams bringing clinical judgement, and vendors supporting safe habits through product design and implementation support.

About this series: This article is part of a series based on independent, peer-reviewed research from Gold Coast Hospital and Health Service. For the complete analysis and all implementation lessons, read our full article.

Continue the conversation: We welcome feedback from clinicians, researchers, and healthcare leaders. Contact our team at clinical@lyrebirdhealth.com

Read the full study: Memon S, Brand A, Taylor B, Michael A, Smithson R. Performance, acceptability, and impact of ambient listening scribe technology in an outpatient context: a mixed methods trial evaluation. BMC Health Serv Res (2025).