Readers Write: From Hype to Headache: The Truth About Ambient Listening

From Hype to Headache: The Truth About Ambient Listening
By Jay Anders, MD and Jeanne Armstrong, MD

Jay Anders, MD, MS is chief medical officer at Medicomp Systems. Jeanne Armstrong, MD is chief medical officer at TouchWorks, Altera Digital Health.

Like prospectors flocking to California in the mid-1800s, hospitals and health systems today are hitching their wagons to AI-powered ambient listening tools in hopes of making their documentation dreams come true.

The attraction is understandable: the power to automatically capture physician-patient conversations and turn them into clinical notes could significantly reduce documentation burden, let clinicians focus on patients, and create a better experience for everyone.

However, as with most gold mining and health tech fantasies, the reality is more complicated. Without the right safeguards, context, and clinical framework, ambient listening risks producing incomplete, inaccurate, or unusable notes. At best, that leaves physicians editing more than they save. At worst, it could compromise patient safety, billing, and care quality.

Transcript 2.0

Every clinician understands the appeal of eliminating clicks and keystrokes. Documentation has become an enormous burden, with 92% of physicians reporting that it negatively impacts care.

Ambient listening promises to capture everything that is said in the exam room, generate a structured note, and let the physician simply review and sign. But as many early adopters have discovered, the first pass is not always the last pass.

Even with high accuracy, the challenge lies in context. If a patient says, “I use my inhaler every morning,” is that a daily maintenance medication or a rescue treatment? If the system places a counseling conversation into the wrong section of the chart, the clinical meaning changes. Physicians cannot uncritically trust the transcript; they must still review and often edit.

Ambient listening certainly removes typing, but it does not solve the core problem of ensuring that documentation is clinically meaningful. This dilemma was echoed recently by the healthcare technology experts at KLAS, specifically:

Our findings show that free text alone will not deliver the outcomes providers expect,” said Mac Boyter, research director at KLAS Research. “For ambient listening to support quality measures, billing, and interoperability, it must generate discrete, structured data—not just nicely formatted notes.

Why context matters

Experienced clinicians know how to ask the right follow-up questions to surface information that patients may not volunteer. They also know which details belong in the history versus the plan and how to translate medical jargon into patient-friendly explanations. An ambient listening system, no matter how advanced, lacks that judgment unless it is anchored by a medical knowledge framework.

That framework provides the “dictionary” against which the AI can validate what it hears. Without it, the risk of hallucinations or misplaced details remains. With it, ambient listening can be constrained, guided, and made more reliable. Context is not a nice-to-have. It is essential to ensure that the note accurately reflects both the clinical encounter and the physician’s intent.

Structured data, not just free text

Another major limitation of most ambient listening solutions is that they generate free text. Even when formatted with section headers, free text is not structured, codified data. It cannot directly feed decision support systems, quality measure databases, or billing workflows.

For example, if a patient’s family history of diabetes is captured only as text, it does not generate a SNOMED code. Downstream systems cannot act on it. Clinicians end up with a nice-looking note that remains invisible to analytics, risk adjustment, and interoperability.

To avoid this pitfall, ambient listening must be paired with technology that converts narrative into discrete, computable data. This makes the output both readable and actionable, while supporting regulatory compliance, coding, and care coordination.

What to look for

Health systems evaluating ambient listening should demand more than transcription and data entry. They should ask:

• Does the system validate documentation against a trusted, clinically referenced framework that is transparent?
• Does it generate codified, structured data that supports billing, quality measures, and decision support?
• Does it give physicians flexibility to toggle between listening, templates, and macros depending on the visit type?
• Does it improve the completeness and accuracy of notes, not just their length?

The answers to these questions will determine whether ambient listening becomes a meaningful advance in healthcare IT or just another short-lived fad.

Help over hype

Ambient listening can make documentation more efficient, but it is not a panacea. Without the right foundation, it risks adding a new layer of complexity instead of solving the problem. To fulfill its promise, ambient listening must be paired with systems that provide medical context, structured data, and clinical relevance.

Again, KLAS’s Mac Boyter reported that its research shows that providers are “looking beyond convenience—they want ambient tools that deliver structured, codified output. Without discrete data, the note is unusable for billing, quality measures, and decision support. Ambient listening is most impactful when it produces information that downstream systems can act on.”

In other words: do not be distracted by the hype. Ambient listening alone is not enough.