Q&A: Duke Health’s Dr. Eric Poon on AI Adoption and the Organization’s Copilot Implementation

HEALTHTECH: What are some of the AI implementation challenges healthcare organizations face?

POON: What I’ve learned over the years is that when it comes to technology, and AI is no exception, many things that could work and should work aren’t going to pan out for a variety of reasons. Part of it may be the technology, the organization’s readiness or whether the end users are appropriately prepared.

What we have found at Duke is that it is important to embrace a “fail fast” mentality. I joke that with AI, you need to kiss a lot of frogs to find your prince or princess. I tell my team that we have to be better at deciding which frog to pick up. Then, when we pick up a frog to kiss, we need to be efficient because not every frog is going to be royalty material. However, when you do find a promising solution, you really need to prepare the organization to embrace the royalty that is coming its way.

Ambient technology has been a good example. We’ve spent the past few years exploring the space, identifying promising vendors and then, through various iterations, incorporating them into our workflow.

Over the past year, we’ve even done a head-to-head trial comparing two leading vendors of ambient technology. We’ve learned a lot along the way and have found amazing results. Not only did the head-to-head trial help us identify which vendor may be better suited for us, it also helped create buzz in our organization so that when we were ready to pick one and deploy it widely, we already had a workforce ready to embrace it. We rolled out that technology to our 5,000 providers here at Duke in early January, and within two months, we already had more than 1,200 providers actively using it daily.

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I think back to my 25-year career in informatics, and I don’t recall any technology that’s been spontaneously embraced by our clinicians this quickly. I will say that we also did a lot of prep work. In addition to that head-to-head trial, we were mindful when rolling out the technology to leverage our existing communication structures so that we had superusers we could lean on, many of whom were early adopters of this technology, so they are able to answer questions for their colleagues.

We were responsive to folks who wanted to start using this technology. We didn’t ask them to wait a long time. And for folks who had questions, we gave them the support and educational materials that they needed.

This has been very successful so far. I can say that even at this early stage, we are seeing favorable results. Last year, when we conducted the head-to-head trial, we got some amazing comments from clinicians almost from day one. We were hearing comments that it was a game changer. It was giving them a couple hours back a day. They didn’t have to spend the night sitting at the kitchen table finishing notes. That was great feedback.

Since the mass rollout, we are seeing early results that show clinicians are finishing their work earlier and closing notes faster. It’s been a rewarding experience, and that’s an important anchor point for us and for the rest of the industry to pay attention to. Not everything is going to work as well as ambient technology, but when you do find something, it’s important to prepare the organization to ensure that you can leverage the success quickly and fully.

HEALTHTECH: What foundational technologies, infrastructure or policies do healthcare organizations need to have in place to support AI initiatives?

POON: It’s important to think about whether you have the right decision-making structures in place. There are plenty of solutions, or frogs, hopping around. So, you need to make sure that you have the right folks in place who can find solutions that can help your organization meet its needs, try them out, and then hold the organization accountable for ensuring the technology is having its intended impact. They also need to be able to let go of those solutions that aren’t quite panning out. That’s something that a lot of organizations can do quickly if they are able to pull together the leaders and focus their limited energies and resources on finding and testing out the right solutions. That’s the one thing I would advise my colleagues to do.

Other foundational elements include having a workforce ready to embrace that technology. I think about our early journey with AI. When it first came out, yes, there was a lot of excitement, but we also made it a point to democratize that technology.

We were early adopters of Microsoft’s Bing Copilot Search, which was free to our organization. We spent some time to make sure that our colleagues of all stripes got an early start using the technology — with appropriate guardrails — so that folks could get comfortable with the tool. That was a small investment that we made early on that is beginning to pay dividends.

We did something similar with Microsoft Office Copilot, for which we bought 300 licenses. It was not free. We quantified the value by collecting data in a pilot to make sure there was some strong signal that the investment would yield benefits, and then opened it up to other leaders who wanted to purchase the tool for staff in their own departments. That cycle of accountability is something we are very proud of having built at Duke.

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HEALTHTECH: Speaking of guardrails, what security controls need to be in place before jumping into AI use cases?

POON: When you’re dealing with healthcare, patient privacy is of utmost concern. We’ve done a lot of work to ensure that every time we implement a new technology, especially if it involves protected patient data, we have a multidisciplinary group thinking about appropriate use and how to pick the right partners.

When it came to Microsoft’s Bing Copilot search, a technical group met to consider whether the technology is secure enough for use with patient data. A different clinical group came together to consider whether clinicians should be using it to perform their clinical duties, which clinical groups would be allowed to use it and what guidelines need to be in place.

We used our governance process to draft a set of guidelines for generative AI in clinical use cases. We made some common-sense recommendations that inform clinicians that if they are using generative AI for clinical care, they should make sure it’s one of the vetted tools that our security experts have approved for their use. Then, when they use it, they need to assume full responsibility for the output. Any clinician who wants to use it needs to be at the appropriate clinical training level to review the output from the AI. So, in some ways, these are common-sense guidelines that have helped us advance the use of AI across thousands of clinicians quickly.

HEALTHTECH: How else do you recommend healthcare organizations go about preparing for AI implementation?

POON: What I’ve observed with traditional IT governance is that while we can be thoughtful about which technologies to deploy, many organizations don’t do a good job of measuring the impact of that technology. A lot of times, we are so busy that we merely implement the technology and move on to the next thing on the list.

However, when it comes to AI, the stakes are higher, and we are still in the early days. So, it’s important to think ahead of time about the success criteria and, perhaps more important, the exit criteria for technology solutions so that you can kiss the frog and either decide it’s a prince or princess or let it go. It’s important for an organization to commit to setting aside the appropriate personnel and time to measure the outcomes of these technologies.

It doesn’t have to be sophisticated. When it came to measuring ambient technology, we used survey tools for the 160 providers who participated in the head-to-head trial. We asked them to tell us how they felt about their documentation workflow at baseline, and then we split them up into two groups — one used technology A and one used technology B — and after a month, we asked them whether they felt their workflows were improved. Then we switched them in the crossover trial and reassessed things after another month to determine which technology they preferred. Having that simple approach, which didn’t take a lot of resources, allowed us to choose which technology would be appropriate for us to deploy.

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HEALTHTECH: How can healthcare organizations measure the ROI of their AI implementations throughout the lifecycle of those solutions?

POON: I’m glad you asked about ROI, because this is something that we are continuing to work on. My personal belief is that it’s important to walk into a project understanding the potential return on investment in terms of either increased revenue, increased productivity or decreased risk, whatever your organization’s goal is. However, it’s difficult upfront, even in a pilot, to prove the ROI outright. So, it’s important to put in the time and effort to measure ROI over time.

For example, with ambient technology, we knew enough from the 160 pilot users’ initial surveys that it was going to improve satisfaction and decrease burnout. We were confident about that, but whether it was going to help clinicians be more productive and help us retain clinicians, we knew that we needed to give ourselves a year at least. We are committed to measuring those outcomes and making appropriate adjustments down the road. That’s the discipline that will help all of us figure out the best investments to make at times when resources are tight.

HEALTHTECH: Is there anything you want to add?

POON: As organizations evaluate new technologies, it’s helpful to consider who the trusted group of clinicians or end users are that you will deploy as your frog kissers. You want to have a spectrum of perspectives, personalities and workflows to make sure your initial group of early adopters can appropriately represent your early majorities. Then you can scale up quickly if you find that the technology is promising. Nurturing that group of innovators who are willing and able to represent the interests of both the organization and their colleagues is a worthwhile investment.