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The Real Barriers to Generative AI Adoption in Healthcare

Where do things break down on the road to AI transformation? A CIO shares anonymously from behind the scenes at a Fortune 500 health system.

When business leaders talk about AI in the media, their comments typically fall into two categories: cautious optimism and excessive optimism. Either way, it often feels more like PR spin and marketing than an honest accounting of what’s actually going on.

You almost never hear, for instance, that AI transformation is incredibly hard.

That’s why we’re launching a new interview series called AI Confessional. We’re having in-depth conversations with CIOs, founders, and executives, and we’re keeping it anonymous so that they can dish about what's really going on behind the scenes at companies trying to introduce generative AI into their organizations.

First up is the Chief Innovation Officer (CIO) of a Fortune 500 health system. She has a special talent for making the very complex intersection of healthcare and tech feel like something that people can actually understand. We asked her about the promise of generative AI to transform America’s troubled healthcare system. And then we asked her for the truth about how these initiatives are actually working out. 

As CIO what was the message you were getting from leadership around generative AI?

Business leaders know they should be doing something, even if they don’t really know what that something is. Their board is asking for their strategy. They know their competition is using it. And they know they have to figure out what generative AI  means for their business and how it fits into their strategic initiatives. The question they face, however, is where to start?

Executives in health systems know how to operate hospitals. They don't necessarily understand advanced technologies like AI. So, they don't ask the right questions about the technology and its potential impact on their organization to get to the unlock. It's not even analysis paralysis, because they're not analyzing things. It’s more like fear of the unknown. Especially when you're in such a regulated environment as healthcare where if anything goes wrong, it has the potential to really go wrong.

So it’s an uphill battle in terms of baseline understanding of the technology.

When people ask, “What is the ROI of Cloud Computing or of AI?” That’s like asking, “What’s the ROI of electricity?” The answer is, “It depends.” What are you using it for? Are you lighting up a school? Or a town? Healthcare executives always ask about the ROI of a given point solution, but they often don’t have enough depth to ask the questions that are keeping them from investing in new tech, especially new platforms. 

Because—and this was my aha moment—no one wants to look bad. They want to do the right thing for their business, for their employees, and for their patients. They’re all smart people, but emerging technology is not their world. In order to cross this chasm you have to lay down a common foundation of understanding. And this is the role many current CIOs are having to play—translating between the tech companies and the traditional enterprises to help them start speaking the same language, so we can finally get the two worlds working together to drive much-needed change in our healthcare system.

There’s little to no incentive for these systems to really do something different.

Where do you see the biggest opportunities for generative AI in healthcare?

There are lots of problems we’ve identified that can benefit from generative AI solutions.There is a massive workforce shortage, and one thing generative AI is really good at is making the workforce you do have more productive. It can help remove repetitive, manual tasks from workers, freeing them up to spend their time on higher order activities their teams or patients need.

The first step, especially for many traditional healthcare enterprises, is to organize, normalize, and make actionable data from across the organization. Once an enterprise has its data organized, even in one part of the company, you can move on to thinking about how generative AI can be applied to your top priorities. One use case I got excited about was around nurse staffing. You can cut down the time that a nurse manager spends doing the books for scheduling and staffing using real-time data and predictive analytics . Another big one is to make operating rooms and the teams utilizing those ORs more efficient. Imagine an Open Table style experience for booking operating rooms. 

Then there’s documentation. The minute you go in as a patient and you're sitting down in front of your doctor, AI is capturing the visit and creating the note so that the doctor can actually look at you and not be typing into the EMR (Electronic Medical Record). I’m excited about the ability to apply generative AI to go up the stack, and start automating coding and expedite prior authorization approvals to help patients get the right care they need, when they need it. 

So there’s interest to bring in generative AI but then you start to hit roadblocks. Where does the resistance come from?

There’s little to no incentive for these systems to really do something different. Their business models aren’t akin to Apple, Amazon, or Google, where profit from one successful part of the business can be used to fund new innovations or technologies with longer payback periods.  

Why would a Chief Information Officer stick his or her neck out? They have to keep the lights on with many legacy technology systems and are facing other mounting short-term ROI pressures from their Board.  

Other roadblocks I often encountered were from teammates who would say, “Our doctors aren’t asking for this specific technology.” Which reminded me of the famous Henry Ford quote, “ If I asked my customers what they wanted, they would have said a faster horse.” Henry Ford’s customers didn’t ask for a car because they didn’t know one existed. But they did have problems being transported in a faster, safer, more economical manner. 

That’s where the spirit of innovation and new technology come together to help solve your customer’s true “job-to-be done”. Customers don’t typically ask about the exact ingredients of a solution - they care more about the solution itself, that it’s safe and secure, and that it is designed to solve their problem at hand. The fact that generative AI is an ingredient in a solution may or may not excite a nurse or a doctor. The fact that the solution solves his or her problem and alleviates extra work from their plate does.

Who is going to win the AI race in healthcare?

Startups have a leg up right now with the talent to build generative AI solutions quickly, but healthcare has never been thought of as a “fast” adopter of technology. I think you’ll see a few startups really land and expand to create more of a platform to support specific areas in healthcare. The ones only focused on point solutions will unfortunately fade away. At the end of the day, it’s not the best technology that wins—which, as a technologist, hurts me to admit! Instead, it’s distribution and scale that win in enterprise healthcare. I’ve seen some of the best technology in the world, but if you have no distribution channel in this industry, it is really hard to gain any real traction. 

Whether you're a big company or small company, it’s a race to land and expand. And I’m excited to see how this revolutionary technology will change our healthcare system for the better. And quickly, I hope!

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