The answer to this question depends, not only on how well AI tools work, but also on how AI technologies are regulated by the FDA. Scott Gottlieb has some thoughts on preferred regulatory approaches in his recent article in JAMA Health Forum.
Artificial intelligence tools with advanced analytical capabilities used in clinical practice, especially tools that synthesize complex clinical information from distinct sources, may automatically be classified as medical devices, regardless of their intended use.
This may be particularly true when AI is integrated into electronic medical record (EMR) software, allowing AI to generate insights that might otherwise go unnoticed by clinicians. Such classification, however, could be at odds with the original intent of laws that were designed to regulate digital health tools based on their clinical use rather than on their analytical sophistication alone, or the sources of clinical data that they rely on.
Why does this matter? Gottlieb argues that were the FDA to classify AI as a medical device, it could impede (or even block) their integration into EMR systems. For instance, using AI may not be legal without pre-market review (i.e., the software could not be used until FDA approved it). Given the speed of development of large language models (LLM) and other AI technologies, pre-market approval could delay AI adoption or could condemn physicians and patients to always using AI versions that are consistently out-of-date.
Instead, Gottlieb argues that AI should be considered as clinical decision support software (CDSS). CDSS is exempt from much of the regulation that medical devices must undergo. This exemption of CDSS from being a medical device was passed through the 21st Century Cures Act, and further substantiated from FDA Guidance on CDSS.
Gottlieb further summarizes his case as follows:
If these AI tools are designed to augment the information available to clinicians and do not provide autonomous diagnoses or treatment decisions, they should not be subjected to premarket review. The FDA could allow EMR providers to come to market with these tools as long as they meet FDA criteria for how they are designed and validated. Then, by drawing on real-world evidence of these systems in action in the postmarket setting, the agency can verify that they genuinely enhance the quality of medical decision-making. Artificial intelligence has an inherent ability to synthesize complex information streams and deliver enhanced analyses or recommendations that might otherwise evade notice. That aptitude alone should not classify them as devices.
While Gottlieb is bullish on AI, an article from HealthcareDive notes that implementing AI can be a challenge. The Peterson Health Technology Institute launched a task force last month aimed at studying the business impact of artificial intelligence on health systems. HealthcareDive explores the challenges of adopting AI with Caroline Pearson, executive director of the Peterson Health Technology Institute.
Pearson notes that one area of success is AI-based documentation.
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.
How are health systems using the AI documentation capabilities?
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
Will AI documentation stick around?
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.
While AI documentation seems like a home run, a bigger question is what AI-based solutions companies are trying to sell health systems and how to measure the value of those investments for health systems. The broader CDSS tools that Scott Gottlieb was discussing above, have not (yet) been widely implemented.
What do you think? How long until we get broad based AI-based CDSS?
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