GoTranscript
>
All Services
>

Public/how Voice Tools Measure Doctors Time Saved

How Voice Tools Measure Doctors’ Time Saved (Full Transcript)

A practical look at using note-edit frequency as a proxy metric to evaluate time savings and documentation quality for clinicians.
Download Transcript (DOCX)
Speakers
add Add new speaker

[00:00:00] Speaker 1: I guess I'm curious you both mentioned how like doctors are busy. You're trying to help them save time How do you express kind of that mission and some of the like outcomes or evaluations that you're ultimately doing? Like are you measuring doctors minutes saved? Do you have some sort of proxy for that? I'm kind of curious outside of like revenue Obviously, it's you know You're trying to grow like what are some of these like metrics you're looking at around you like voice product that you're measuring this As outcomes for us we see how how much the time they spend like editing the notes So how often they edit the notes?

[00:00:28] Speaker 2: Okay, and this is like something that tells us like whether we did a good job or not, basically, you know Then from that we can kind of roughly infer. What's the time saved? So and I would say that happens rarely so it's doing pretty good job.

[00:00:41] Speaker 1: So you're actually doing time save. Yes

ai AI Insights
Arow Summary
The speakers discuss how to measure the impact of a voice product designed to save doctors time. They evaluate performance by tracking how much and how often doctors edit generated notes, using that as a quality signal and a proxy to infer time saved. Speaker 2 notes that edits happen rarely, suggesting the system does a good job, and confirms they do measure time saved.
Arow Title
Measuring Time Savings in a Voice Notes Product
Arow Keywords
doctor time savings Remove
voice product Remove
clinical notes Remove
note editing frequency Remove
metrics Remove
outcomes evaluation Remove
proxy measures Remove
Arow Key Takeaways
  • A key success metric is how often clinicians edit the generated notes.
  • Lower edit frequency suggests higher note quality and greater time savings.
  • Edit behavior can be used as a proxy to roughly infer minutes saved.
  • Beyond revenue, operational metrics tied to clinician workflow are central to evaluation.
Arow Sentiments
Neutral: The tone is inquisitive and factual, focused on practical measurement methods (note edits as a proxy for time saved) without strong positive or negative emotion.
Arow Enter your query
{{ secondsToHumanTime(time) }}
Back
Forward
{{ Math.round(speed * 100) / 100 }}x
{{ secondsToHumanTime(duration) }}
close
New speaker
Add speaker
close
Edit speaker
Save changes
close
Share Transcript