The Peterson Health Technology Institute launched a task force last month aimed at studying the business impact of artificial intelligence on health systems.
Implementing AI tools can be a challenge, as providers need to be wary of safety, accuracy and equity concerns that could come with the emerging technology. But rolling out safe products isn’t the only question for health systems implementing AI: They also need to figure out how to measure success.
The institute, which has previously worked to evaluate other digital health tools, is pulling together health system leaders to discuss how they measure financial, efficiency and workforce effects of AI products.
Caroline Pearson, executive director of PHTI, joined Healthcare Dive to discuss the task force, including the group’s first focus areas.
This interview has been edited for clarity and length.
HEALTHCARE DIVE: Can you tell me a little bit more about what the task force is and how it will work?
CAROLINE PEARSON: So we formed a task force because we really wanted to understand how some specific AI solutions were being adopted in health systems, and how they were thinking about measuring the impact of those solutions.
We started with a pretty narrow focus on AI tools for documentation — AI scribes — and revenue cycle management, which are two of the fastest areas of AI adoption that are currently happening in health systems in this country. We wanted to talk with executives of those health systems to really understand how they’re thinking about implementing those AI solutions and what the business impacts of that implementation are, from both a financial perspective, efficiency perspective and a workforce perspective.
Why do you think this task force is needed for health systems?
Permission granted by The Peterson Health Technology Institute
Health system leadership are working to both adopt technology quickly and struggling to figure out how to measure that and think about its business implications as they go. The leadership has been really excited to share best practices with one another and learn how other systems are doing this, and also to think about what kinds of data and metrics they want to be measuring for the future.
So each of the systems has made their own business case about how they’re thinking about these initial purchases on the documentation side. But over the next several years, how will they continue to be able to measure the impact of those solutions on their delivery systems? And what kinds of data are they going to need out of the AI tools in order to do so effectively?
You mentioned you wanted to focus on documentation and revenue cycle products. Is that because they’re popular, or are there any other reasons why you decided to hone in on those two areas?
I think that documentation is going to be the fastest adoption of health technology that we ever see. It feels like they’ve gone from zero to very widely adopted in less than two years. And so part of it was really driven by that growth.
But both rev cycle management and documentation are places that are really at the business heart of how a health system operates, right? It really affects productivity, patient throughput, as well as patient experience and provider experience. So they were very good places to start, because they’re just brass tacks issues around how health systems operate.
So it’s a way of analyzing, “Sure, you’ve adopted this documentation assistant. But is this actually saving you time? Is that allowing you to see more patients? Is it reducing provider turnover?”
Yeah, exactly. Some systems are looking to increase the number of patients that they can see in a given clinic. Others are really focused on provider burnout and trying to reduce the “pajama time” that they’re requiring of doctors to do documentation, and then ultimately reducing turnover.
And yet, other systems are really thinking about that patient experience and how to make sure that every clinical interaction is giving the doctors enough time to spend face-to-face with their patients. All of those are valuable outcomes, but systems want to be clear-eyed about what their expectations are for each of those and how they will know whether they’re accomplishing their goals.
Health systems will likely still focus on containing costs this year. Do you think that might also push them to consider eventually dropping some of these AI tools if they’re not worth it in the long term?
I don’t know. My sense is that these tools, the documentation tools, are quite sticky, and once providers get used to using them — and patients feel comfortable with them — they will be hard to remove from the exam room.
However, we’re also seeing those tools become commoditized more rapidly. So the price may come down as that market is quite competitive. And then the question is, what other solutions are those companies selling into the health system? And how do we begin to measure the value of those investments?
So, eventually those companies might start adding to their offerings, and health systems might be more interested in saying, “I’ll pick up this AI documentation company that might also offer me something else.”
Yes, and we’re seeing that happen very quickly, a sort of spreading of these AI solutions within systems. And rev cycle is the sort of first and most obvious place that AI solutions grow out of documentation.
Once you’ve captured the entire patient encounter, you have all of the data to convert that into billing. And so it may be that the price of the AI scribe becomes pretty efficient, but the price attached to all the revenue that you generate off of that encounter is much more valuable. Thinking about how you measure those other lines of business is important.
