HIMSS24: The Year When the Artificial Became Real (or At Least, Realer)
As a 32-times veteran of HIMSS Conferences, I feel as though I have some standing to opine about trends over the years in themes present in the conference. Indeed, the annual HIMSS Conference, sponsored by the Healthcare Information and Management Systems Society, as evolved dramatically—if gradually—over the past several decades. I still remember my first HIMSS Conference, back in 1991, at the Moscone Center in San Francisco, and what it was like. Back then, twenty years before the passage of a federal law required the vast majority of hospitals to implement electronic health records, the HIMSS experience honestly was a rather strange one. Walking the exhibit floor, there was no clear overarching theme; instead, a wide range of vendor companies—some of them reportedly pitching solutions that were still in the “vaporware” phase of development—hawked a very wide range of answers to healthcare’s challenges.
Well, that was more than three decades ago; and these days, there is a unifying overall theme that is unmistakable that is driving everything: the exploding cost of the U.S. healthcare system—the Medicare actuaries are telling us that we will be going from the current approximately $4.6 trillion in annual expenditures, to a mindblowing $7.2 trillion within the next eight years—is pushing the entire healthcare system towards action. Or, to be more precise, the purchasers and payers of U.S. healthcare costs—the federal government, state government, employers, and the health plans that pay for the healthcare system’s expenditures—are demanding that the providers of healthcare—the hospitals, physicians and medical groups, and integrated health systems—curb costs and improve patient outcomes.
Of course, the underlying factors involved here are daunting in the extreme: the number of Americans ages 65 and older is projected to increase from 58 million in 2022 to 82 million by 2050, with that group’s share of the population rising from 17 percent to 23 percent; meanwhile, the proportion of Americans living with one or more chronic diseases is absolutely exploding now. And those two factors alone are fueling the core explosion in healthcare costs. At the same time, labor costs are exploding, especially the costs of employing clinicians, and most especially, the costs of nurse staffing.
All of these factors are pushing forward the phenomenon of value-based, including risk-based, contracting. But that shift from volume-based to value-based reimbursement alone will not be enough, and everyone knows it: everyone working in the healthcare system will need better and better tools, including analytics and productivity tools, to change the equation and improve the value of healthcare.
And so the moment for artificial intelligence (AI) and machine learning (ML) has arrived. It’s not that AI is actually new; depending on how one defines artificial intelligence, numerous speakers and panelists noted last week at the Orange County Convention Center in Orlando, forms of AI have been around for years. The difference is that, for the first time, a very large percentage of patient care organizations are now actively plunging into the development of AI—both “traditional” AI—meaning AI architected through the development of formal algorithms—and generative AI. Of course, under the umbrella of those two broad categories are a vast range of different strategies being pursued healthcare system-wide, by groups of individuals coming out of a very wide range of disciplines, from IT/programming to data science to clinical and operational backgrounds, all with the intent to improve diagnostics, optimize clinician workflows and clinician productivity, address staffing shortages and costs, and ultimately also better connect clinicians and care teams to patients—all in order to curb costs and improve outcomes.
And so AI was on everyone’s lips this year at HIMSS24; it really was inescapable. And what was fascinating for me was the level of realism expressed by all the speakers and panelists whom I heard from in sessions at this year’s conference. I honestly didn’t hear a single individual expressing a hyped-up view of what’s happening now or what will be coming in the next few years; indeed, I heard the opposite: a very sober, realistic set of perspectives on what’s happening now, and what’s ahead.
For example, when asked how he and his colleagues at Mayo Clinic are moving forward to create and use AI, David McClintock, M.D., chair of the Division of Computational Pathology and Artificial Intelligence at Mayo Clinic, said in the course of a panel discussion entitled, “Digital Transformation from Screening, Diagnosis to Personalized Care,” that “It comes down to what the clinical use case is. We’re really looking at bringing in multiple different areas, and bringing in data to develop a model, in ways that won’t add additional steps to process, while also making sure you don’t cause harm to patients,” Dr. McClintock said. “You have to bring all those different aspects together. I had a friend of mine who’s a pathologist, who said, hey, I have an algorithm I’ve built and published a couple of papers on it, can you help me deploy it? It seemed like an innocent request. But I found out he used a third-party solution, there was cybersecurity risk, the workflow was all manual to execute on the algorithms; and I asked him, how do you think the clinicians will react? And he said, well, I’ll add an addendum to the end of my paper. So when you think about building out solutions, you have to begin to think about how to build a roadmap. What are the resources needed? And will clinicians want to use the algorithm clinically? And who will support it? All those things, including the regulatory aspect, need to be put together.”
The approach that McClintock and his colleagues are taking at Mayo Clinic is far from unusual; indeed, it seems to be the norm. And there is an understanding among the leaders leading these AI initiatives, that this work will necessarily be highly iterative and that AI will perforce have to evolve greatly over time and with experience. Another member of that panel, David Vawdrey, Ph.D., chief data and informatics officer at the Geisinger Health System, put it this way: “Technology is moving forward fast. My new car has all components that make my life easier and safer, but it’s not a self-driving car. With generative AI, we’ll hit the trough of disillusionment, but eventually, it will produce results, maybe not the ones we expected; and then there will be a generative AI 2.0.”
Many other speakers and panelists shared perspectives aligned with what McClintock and Vawdrey said on Wednesday. And the bottom line is that every single speaker and panelist I heard fully acknowledged that this will be a long, complex journey forward for everyone. In other words, everyone seems to be taking a very sober, realistic approach to all of this. And everyone whom I saw speak at HIMSS24 about AI, emphasized how long the journey forward will be, and how complex all the processes around AI development, will continue to be.
So I have to say that the discussions I heard at HIMSS were heartening, both because patient care organizations are actively leaping into the ring—a show of hands at that particular panel discussion found that nearly 90 percent of audience members indicated that their organizations had already begun working in this area—and because no one I heard spoke in a Pollyanna-ish or cheerleader-y way.
And so yes, the talk at this year’s HIMSS Conference was very much dominated by AI and machine learning. But I was encouraged by the realism of all the speakers, about the subject. And I look forward to hearing the discussions in 2025 and 2026, and to see how leaders in U.S. healthcare move forward to make AI a reality that will increase productivity, support our clinicians, enhance care management, and better connect patients to providers—and yes, also help provider organizations master their revenue cycle management challenges—going forward. So it was a HIMSS Conference with a lot of very real talk about artificial intelligence—and that’s a very good thing indeed.
