Accelerating the integration of digital healthcare tools in a responsible, clinically meaningful way is the primary focus of the American College of Cardiology’s (ACC) innovation agenda, according to John Rumsfeld, MD, PhD, who spoke at the 2018 Cardiovascular Summit in February.
Technology leaders contacted Rumsfeld, the college’s chief innovation officer, after the ACC published a “roadmap for innovation” in 2017 (J Am Coll Cardiol 2017;70:2696-718). They’re eager to collaborate with cardiologists in the hopes of robustly but quickly validating new technologies that could improve the cost and effectiveness of care, Rumsfeld says.
“[T]o make this work, rather than being passive participants in this technology transformation like we were with the implementation of the electronic health records … we want to help lead the digital transformation of healthcare,” explains Rumsfeld, who also is a professor of medicine-cardiology at the University of Colorado. “We know that there is risk involved in this in that not all technologies will be successful. But I think that unless we guide these technologies to the right clinical settings and how to use them optimally in the right clinical settings … we’re still going to have the same healthcare system delivery problems we’ve had for the last 50 years.”
In an interview with Cardiovascular Business, Rumsfeld divided digital healthcare into three phases: virtual care, remote monitoring and artificial intelligence (AI)-driven care. He thinks of the phases as “parallel bowling lanes,” each with specific challenges that can be tackled concurrently.
>Virtual care applies the idea of in-person visits to serving patients in other locations via virtual visits. This concept is “ready for primetime,” Rumsfeld says, but only if payment models align. He’s aware of payers reimbursing health systems for virtual visits to the same extent as they do for in-person care, but it remains uncommon.
“Right now, in many settings in the U.S., productivity is judged by how many in-person visits you are conducting,” he says, “so, until that paradigm shifts that those interactions with patients could also be virtual and count toward your productivity, we’re not going to get very far.”
>Remote monitoring faces payment-model hurdles as well as other barriers to implementation, according to Rumsfeld. Wearable technology and biosensors could contribute to data overload for clinicians and be a challenge to integrate into clinicians’ regular workflow.
Then there’s the fact that “most of the studies of remote monitoring to date haven’t been positive,” Rumsfeld notes. “In other words, even when we are armed with extra data from home, for example, on our patients, it hasn’t necessarily led to care decisions or management decisions that have translated into better outcomes for the patients or to more cost-effective care. But that doesn’t mean we’re giving up on the concept.”
> AI-driven care is the furthest away from widespread adoption in clinical practice, Rumsfeld predicts. It has the challenges of aligning payment incentives, deriving meaningful information and showing evidence to support its use, plus the added complexity of requiring interpretation.
“If it’s outside of healthcare and you’re doing something like recommending a new movie or a new book or another product, I think we’re willing to take the advice of the artificial intelligence,” Rumsfeld explains. “In healthcare, the stakes are much higher. We rightfully require an understanding of why artificial intelligence is recognizing an image a certain way or predicting a certain outcome for a patient or recommending a specific therapy. We’re going to have a clinical skepticism about that unless we can understand how it’s coming to that recommendation to a certain degree.”
He expects that AI applications for cardiac imaging will be among the first success stories in this phase. “If you have an artificial intelligence–driven interpretation of an image, it’s still very easy for us as clinicians to compare it to the actual image and understand how close the AI is,” he says. On the other hand, AI for risk prediction or recommending therapies is tricky because the underlying data are worse and the mechanisms for how the algorithms come up with those results are less easily understood.
“I think we need to have a high bar of evidence and be very wary of unintended consequences before we adopt them in clinical practice,” Rumsfeld says. “And at this point that still seems far away.”
Different burdens of proof
Though Rumsfeld preaches caution for implementation of some AI-driven platforms, he says the goal of the ACC’s partnerships with the technology industry, academic research centers and other stakeholders is to streamline development and testing of digital healthcare solutions.
“Rapid-cycle testing is a major focus” he says, adding that technology companies need their medical partners at least as much as the medical industry needs them. “They are otherwise a little bit isolated to show the clinical utility of these tools, and they also are often under quite a bit of time pressure, because with a certain amount of capital from venture capital or seed funding they only have a certain amount of time to develop a product and show it can work.”
He believes traditional academic research cycles will take too long, “especially for startup companies,” so the focus will need to be on “creating relationships where we can more rapidly and efficiently evaluate these things in clinical practice.”
Rumsfeld predicts deep-pocketed companies such as Google, Facebook, Amazon and Apple are the most likely to disrupt the healthcare industry as much for their human resources as for their capital. When they acquire smaller companies, he points out, it’s as much to accumulate the brain power of those employees as for their products.
Regardless of how it happens, the ACC wants to drive the digital healthcare transformation, not be a passenger. “It’s easy to see in the healthcare environment that something has to change,” he says. “The continued rise of cost of care, the continued recognition of unexplained variation of care and waste in the system has not gotten better despite the availability of technology.
“We think part of that reason is there hasn’t been this overt bridge … between the clinical enterprise and the technology enterprise. If we can cross that bridge, we do think care could be more efficient and transform how care is delivered in a way that’s good for patients and good for clinicians in the health system.”