In a recent episode of the AHA Associates Bringing Value podcast series, Kathleen Wessel, Vice President of Business Management and Operations at the American Hospital Association, sat down with Steven H. Rube, MD, FAMIA, Chief Medical Officer at IMO Health.
Wessel and Rube explored one of the most pressing questions in healthcare today: How can we effectively use artificial intelligence (AI) to improve documentation, clinical data quality, and workflow efficiency – without compromising clinical intent or disrupting patient care?
You can listen to the complete podcast episode here.
Short on time? Keep scrolling for key takeaways from the discussion, including audio clips.
1. Preserving clinical intent requires more than transcription
Wessel opens the discussion with a core question: How can AI help without compromising the meaning of clinical data? Rube responds by emphasizing that AI must advance from capturing words to understanding semantics.
“An AI system or an ambient AI system… needs to be more than a fancy dictation system.” he says. “We’ve had the ability to listen and record and voice-to-text for quite a while, so why is this different? We’re going to need these systems to not only listen to what we’re saying, but to really understand what we’re saying.”
Rube gives a real-world example:
2. Effective AI integration must feel “invisible”
When asked what successful AI implementation looks like, Rube argues that AI should enhance the clinician–patient relationship, rather than intruding upon it.
“The moniker that doctors are averse to technological changes, I take offense to; I don’t think that’s true,” he says. “Doctors ask two very simple things: ‘Make me better and make me more efficient.’ And part of the ‘Make me more efficient’ is ‘make these solutions intuitive.’
Rube elaborates further:
3. Reducing burnout means reducing data entry
Rube is candid about doctors’ shortcomings when it comes to data entry and the toll that electronic health record (EHR) documentation takes on them and all clinicians:
“ We not only don’t want to be data entry people – we’re very, very bad at it,” he says. “Even the ones who tell you they’re good at it – they’re bad at it. We have too many other things going on… Most of us did not take typing classes.”
Rube shares a personal anecdote that captures this sentiment:
4. To measure AI’s value, track both subjective and objective metrics
Healthcare leaders, Rube says, should evaluate both subjective data, such as clinicians’ attitudes about their workplace, and objective data, such as the severity of patients’ sicknesses before and after ambient AI implementation.
When it comes to those subjective elements, Rube suggests asking, “Are physicians happier with it? Are they able to, you know, finish their day? Are you able to recruit a higher level of employee because your systems themselves are not obtrusive and driving people away?”
He also touches upon some key objective metrics, including those relevant to value-based care organizations:
5. AI’s future includes real-time intelligence at the point of care
Rube ends the episode by noting how we’re at an inflection point, similar to when we transitioned from paper charts to EHRs:
“We have to stop thinking of the electronic medical record as just the ‘paper record, but on a computer,’” he says. “…these AI systems will be able to accurately describe a patient in real time to the doctor and present that information in real time when it’s needed.”
He explores the many possibilities of moving from “generative AI gathering information” to “agentic AI”:
