What Is Ambient Listening in Healthcare? Why It Stuck
Ambient listening in healthcare is AI that listens to the conversation between a clinician and a patient, understands its context, and drafts the clinical note and structured fields in the medical record for the clinician to review and approve. Paul Uhrig, a healthcare lawyer and former chief legal and digital health officer at Bassett Healthcare Network, told Lori Ellis on Open Door Salon that it won over many clinicians because it solved a widespread problem close to the physician, with an immediate benefit. He said he had sat in the room with clinicians seeing it for the first time and watched them react to a capability they had not seen before.
How does ambient listening work?
It captures the visit and turns it into documentation. Lori Ellis asked Uhrig to explain it for listeners outside healthcare:
The ambient listening technology allows the technology to effectively listen in to the interaction between the clinician and the patient and then it contextualizes that conversation.
He described what that contextualizing involves: telling who is speaking, separating a joke or a casual aside from clinical content, and identifying the symptoms being discussed. Linked to the medical record, the AI then drafts the documentation every encounter requires. Uhrig explained the two parts of that record:
Every physician needs to record an encounter and it records that encounter in the free notes section, right? Which is just writing thoughts, and then there are these structured fields that need to be filled out.
The AI drafts the note and populates those structured fields for the clinician, which is the work that used to take the time.
What problem was it solving?
Clinicians splitting their attention, or giving up their evenings. Uhrig described both versions:
The physician or the clinician is on the computer typing in their notes real time rather than being focused squarely on the patient.
The alternative was catching up later, at home. Clinicians, he said, would have to:
Work at night to catch up with his or her notes, right? What was called pajama time.
Clinicians went from patient to patient during the day and wrote their notes in the evening.
Why did clinicians take to it?
Because the problem was common and close to the physician, and the benefit was immediate. Uhrig described the starting point:
It was such a prevalent problem that was being solved.
He acknowledged that, as with all of these technologies, there are advocates and detractors. For the advocates, the change was concrete:
One, it allowed them to focus on their patient during the encounter. And then it just helped them on their workload, right? It helped them be more efficient, more accurate. Again, they got to review it, but be more accurate.
He added that it gave time back outside work:
They could spend now more time with their spouse or their children, right?
In his account, that tangible, immediate effect is why it captured the imagination of so many clinicians.
Does the clinician still control the record?
Yes, and Uhrig counted that as part of why it stuck. The note is a draft:
Now the clinician needs to go in and review them, make sure they’re accurate and approve them.
He returned to the point when explaining the adoption:
They also could stay in control, right?
The record stays theirs: they can, and should, read the note and confirm it is accurate. He put it this way:
So it wasn’t just behind the scenes, if you will.
That review is the human check on AI-generated content, the same question behind our earlier post on who is liable when AI makes mistakes in drug development.
How widely are health systems using AI?
Widely, by the figures Uhrig cited, and moving toward clinical use:
The latest statistics are that I’ve seen is about 85% of health systems are using AI today, whether it’s administrative functions, clinical functions.
He said many systems started on the administrative side and are moving quickly into clinical work, with ambient listening the most prominent example. The goals, in his account, are access and outcomes:
In ways that are seen by the patient and maybe in ways that aren’t always seen by the patient.
Other AI rollouts have a harder time; our earlier post on why hospital AI pilots fail to scale covers what separates the ones that stall.
What does ambient listening teach about AI adoption?
That clinicians adopt what they can feel. In Uhrig’s account, ambient listening worked because the problem was visible to everyone, the benefit was immediate, and the clinician kept authority over the record. Patients are bringing AI into the same encounter from the other side, as our earlier post on how AI is changing doctor-patient conversations describes.
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This post is drawn from the recorded, on-the-record conversation between Lori Ellis and Paul Uhrig on Open Door Salon.