CIOs dish on AI automation strategies that work
Artificial intelligence machine learning are already making some intriguing potentially transformative impacts on the way healthcare is delivered, from the exam room to the diagnosis to ongoing care management beyond.
But it’s important too to keep the promise limitations of automation augmented intelligence in mind. At HIMSS22, three clinical IT leaders from major health systems offered some insights into how they’re deploying AI ML – from predictive analytics to EHR automation to value-based care population health management.
Each emphasized that, despite the huge potential, it’s still early days.
Importantly, it’s key to not get too excited about the technology itself wishcasting about the wide array of challenges it can solve, but to focus instead on smaller, discrete, achievable use cases, said Jason Joseph, chief digital information officer at Michigan-based BHSH System.
“I think we’ve got to look at this area of deep analytics more holistically, with AI being a piece of it – but really focusing instead on what problems we’re trying to solve, not necessarily the AI,” he said.
Dustin Hufford, CIO at New Jersey’s Cooper University Health Care, is also taking a cautiously optimistic view, moving slowly deliberately in its AI implementations.
“It’s certainly something that we really need to think about in terms of the safety around AI the equity part of it: Are we building our own biases into the software that we’re building?”
But there’s no denying that “this is really gearing up right now,” he said. At Cooper University, “we focus on governance around digital, which includes a lot of our AI technologies that we’re going to implement in the next couple of years.”
Ensuring C-suite buy in is also key, he said. “How do we engage the highest levels of the organization in the planning understanding of what we’re looking at here? We spent a lot of time last year understanding how the mechanics were going to work, the transparency, now we’re getting into the nitty gritty of it.”
Step two, said Hufford, “is to really define what are those exact things when it comes to something like AI? What’s the exact thing that we need to measure to make sure we’re hitting the mark on this?”
Dr. Nick Patel, chief digital officer at Prisma Health in South Carolina, sees a lot to like when it comes to small-AI use cases like workflow automation.
“We as providers are constantly doing repetitive activities that can be automated over over again,” he said. “I didn’t go to medical school to click my way through taking care of patients.
“Medical school is all about gathering information, learning about anatomy, physiology, disease states, then applying that to humankind in order to get them to their goals keep them well,” he explained. “But when you throw a layer of EHR in there, you lose a lot of that because you’re having to snip into how do you get all this information so you can make a good clinical decision?”
At Prisma Health – which is also undergoing a larger digital transformation that Patel will talk about Thursday at HIMSS22 – the question is “how do we start to automate those processes, so we’re actually using our neurons to actually take care of patients, not trying to figure out the system?”
Bigger-picture, Patel is more excited about AI-enabled analytics for population health.
“A typical physician, their panel is about 1,500 or 2,000 patients. You can’t really make a huge impact in the national narrative when it comes to population health, when you’re seeing 2,000 patients per year. So what we have to start to think about is we use bigger data.”
But it has to be “cleaner data,” he added. “You can’t layer in machine learning AI, all these advanced tools until you make sure that the actual data is actually aligned clear. Because if you do, then you’re going to get insights based on false data – that is extremely dangerous.”
The main thing here, from a governance standpoint, “is to really understand, what are all your style of data pieces, what are the discrete non discrete data platforms, how does that all converge?” said Patel.
“When you think about diabetes hypertension, what parts can you automate? I would venture to say as high as 80%. Using the right data in order to get the right insights to the providers so they can be empowered to take care of those patients better.”
Joseph says he’s similarly optimistic about the prospect of AI-empowered value-based care.
“I’m going to differentiate maybe machine learning predictive analytics from true AI,” he said. “There is a huge opportunity there to really start to understwhat are the drivers for the population.
“For example, diabetes hypertension – some of those are either under-diagnosed or, if they are diagnosed, there are no interventions. What you need is the ability to surface that stuff, based on the data that’s sitting there at rest, surface it push it forward.
“And that all has to be run through some analytics,” he said. “You can do it based on rules you know, if you’ve got all those rules. And in other cases, you can look at historical patterns, which is where I think you could start to introduce some AI that’s just looking at the trends that exist out there using the data you have, which is better than nothing.”
But in all of those situations, what we’re really talking about is using augmented intelligence,” said Joseph. “What we’re talking about is very imprecise AI. At this point, it’s going to give you maybe an, ‘Eh, start here, start here.’ But as we get more advanced with our clean, cleanliness of data, we start capturing more data, we start to get more more precise to the point where it could become fully automated.”
At the moment, he said. “I’d rather know a 60% chance than not know anything. And if I can look at an image say this thing is 82% likely to have this diagnosis, well, that ought to help these radiologists make a diagnosis. Over time, you could probably get that to 90%, 93%, 95%, 98%.”
But soon, the ethical challenges may increase as the technology evolves.
“At some point we’ll have an ethical decision about when the computer makes the diagnosis. Then the next step will be for the computers to prescribe medication, or order the procedure.”
But “that’s going to take us years,” said Joseph. “What we need to be doing along the way is making our systems better, making our processes better, making sure the data is cleaner, introducing these things along the way so that [the models] can learn be more accurate over time.”