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Do digital tools fail to show impact in high-risk, high-cost populations?


Top-funded private digital health companies in the U.S. have yet to show that they significantly affect outcomes for high-risk populations, according to a new study.

In general, these companies tended to publish studies on healthy populations—when they published studies at all. Many didn’t, according to the researchers, whose paper was published in the January issue of Health Affairs.

“These top-funded companies haven’t demonstrated the kind of standard of evidence regarding impact that physicians, hospitals, patients and payers are probably looking for,” said Kyan Safavi, lead author and the David F. Torchiana health policy and management fellow at the Massachusetts General Physicians Organization. Instead, they tend to study healthy populations, rather than high-risk, high-cost populations.

But enthusiasm about digital health companies’ products—including biosensors, artificial intelligence-based tools, and population health tools—is high, with venture funding numbers to prove it: In 2017, digital health companies raised almost $6 billion. The funding is driven partly by the hope that these technologies will increase connectivity, patient engagement, and coordination between patients and providers, according to the researchers.

Whether they can do that in high-risk, high-cost populations is relatively unknown, though, with few published peer-reviewed studies on the matter. “We think right now we’re in a validation phase of many of these technologies,” said Adam Cohen, a co-author and the health technologies program manager at the Johns Hopkins University Applied Physics Laboratory. “What we hope is coming is a greater proof-of-efficacy phase after validation.”

The researchers looked at 20 digital health companies in the U.S. with the most private equity funding through April 15, 2017—a group that had median funding of $67.5 million and, altogether, 156 published studies on their products and services. Fewer than a third of those studies focused on patients with high-burden conditions or risk factors.

One reason for this dearth of studies on high-risk, high-cost populations is because many digital health companies work with direct-to-consumer models. That often means that they sell products that promote wellness and target already healthy populations.

What’s more, there is no regulatory requirement for these companies to study high-risk, high-cost populations, Cohen said. Regulatory requirements could shift the environment to one that promotes studying high-risk, high-cost patients; value-based incentives for adoption could also do that, the researchers wrote in the study. “If value-based purchasing becomes the dominant model, digital health tools that have evidence-based value will be in demand,” they wrote.

But, they noted, the shift to value has been slow, and fee-for-service models still dominate. For that reason, they suggested directly giving providers incentives to adopt certain digital health tools, such as those that help them better coordinate care.

“Overall, we think digital health holds great promise, such as the ability to reach millions of patients at scale and being relatively cheap,” Safavi said. To meet that promise, he added, digital health companies must prove that what they’re doing works, including in high-risk, high-cost populations.