When it comes to regulating medical devices, the status quo of relying on premarket evidence has its limitations: it’s expensive, it’s time-consuming, and it isn’t necessarily the best data to show how something truly operates in everyday life. But, according to a panel at the Connected Health conference in Washington, D.C, digital health tools that generate a trove of data could put us on shorter paths to regulatory clearance, albeit with some challenges.
“The best evidence is in the real world, post-market news about a device. It’s what happens to us or our families and loved ones when they used them,” Robert McCray, senior advisor of the Personal Connected Health Alliance said at the Connected Health Conference in National Harbor, Maryland. “But the problem is there isn’t a way to collect and present that data in a way that would support medical device validation.”
But, McCray pointed out, we are getting closer, both in terms of funding and in policies like the 21st Century Cures Act, which supports the Precision Medicine Initiative and other programs that depend on more and better information from real world evidence. Additionally, the FDA is adapting to keep up. The agency released draft guidance on the use of real-world evidence for medical device qualification in July, and FDA Chairman Robert Califf has described the use of RWE to inform decision making as a top priority.
Embodying that priority was panelist Bakul Patel, the Associate Center Director for Digital Health at the FDA. Representing the agency’s first employee with “digital health” in his title, Patel discussed the ways the FDA is looking to use real world evidence from software-based devices in the same way many health professionals already are.
“In our guidance, we touch on three things – what exactly is this data and evidence? How does it provide insight not only for healthcare decision-making but also regulatory decision-making? And how do we take that to the next level?” Patel said.
Patel echoed McCray’s concern that even though we may have the data, putting it all together in a way that informs regulation is another challenge.
“In the next five years, what the industry and the FDA has agreed on is to bring all of these pieces of data. The patient is a big part of that. As we think about new paradigms in the world of digital health and we use things as a springing off point, we have some things to consider,” he said. “Does our tolerance of risk change? How are we collecting the data? We are on this journey and we have to figure out how to connect things.”
In other words, digital health companies should get very comfortable with the fact that they will need to work closely with the FDA to achieve these goals. David Price, VP of Medical Affairs for Dexcom, said this should be a welcome opportunity.
“Use your partner: the FDA. They are your partner, they are your friend, they are not a foe,” Price said. “When seeking clearance, make sure you contract or work with a real regulatory person. These premarket submission meetings are incredibly useful.”
Building on that, using real world evidence as device makers are looking to build new products or expand on another should be approached the same way. Price explained the impact of personal anecdotes of users of Dexcom’s line of continuous blood glucose monitors. While the device was not regulated based on real world evidence (it has components of a Class 3, but was submitted for de novo clearance in order to de-regulate down to Class 1), the feedback they got from people encouraged Dexcom to look at more indications for the device.
“We had a group of over 30 people who came forward to talk about their experience and it had a significant impact on the advisory panel,” Price said. “This resulted in us going to the FDA to start talking about more ways to use the device, like extending the indication to replace fingersticks.”
While digital health companies and the FDA are trying to figure out how to collect and streamline real world data, patients are already leveraging their data from tools like CGMs and social networking to manage their health. NightScout, an open-source website for people or relatives of those with diabetes, was started by patients who wanted to remotely monitor CGM data. It started specifically for the Dexcom G4, and now integrates with the Share feature of the G4, G5 and Medtronic devices for both iOS and Android.
“This is completely homegrown and not under the purview of the FDA, but we are an example of how you can take real world data, come together and decide how to make devices and software work better for patients,” said Gail DeVore, a panelist who has been living with type 1 diabetes for 45 years. “People are pushing the envelope and going beyond the solutions marketed to them.”
McCray pointed to DeVore as an example of the ways real world evidence can inform regulators and device makers on methods to bring data together in a meaningful way.
“The promise of real world evidence is that we can have patients like Gail where all of us have access to those extensions of technology, faster, and not have to make it themselves,” McCray said. “These are solutions that could come as fast as companies like Dexcom or Apple or Google can effectively make platforms to extend technology for medical devices.”
Additionally, device makers can use that type of real world evidence from patients to see patterns to inform how they can build extensions of the existing technology. Since patients using NightScout share both insulin and glucose data, device makers can get a more complete patient profile than their platform alone may provide.
“From our standpoint, what we are trying to do is understand the individual patient problems,” Price said. “If we see patterns of highs and lows that we could use to inform the clinician, we could potentially begin to offer insight on how to adjust. If you have both sets of data together, you can create algorithms that would be helpful to create new systems.”
To facilitate this, Price said, Dexcom is moving into the world of decision-support and is partnering with those who are doing the same. He couldn’t disclose information at the time, but said Dexcom will be making public announcements soon of partnerships to innovate new solutions.
That extra patient data also offers up research opportunities that would otherwise be hindered by information siloes, said Joyce Lee, Associate Professor at the Department of Pediatrics and Communicable Diseases at the University of Michigan School of Public Health.
“There is research happening all the time in the course of clinical studies, and the idea from any general learning system is that you need to create ecosystems,” she said. “But coming from a health system perspective, there are a lot of barriers to real world evidence. EHRs are basically a Microsoft Word document.”
With real world evidence and use cases that are available via platforms like Nightscout or even Facebook, Lee said opportunities for innovation emerge because device makers and clinicians can see a fuller picture. While this is the idea with many apps or digital health management tools that may not need FDA regulation, the same metrics could be applied to regulated devices. Critically, the panelists said, data sharing should be the central goal of clinicians, patients, regulators and device makers.
“There is all this missing patient data, missing data on how devices actually perform in the real world, because we all live in our siloes,” Lee said. “We need to come together because we all have different strengths.”
An important fact to consider, however, is the actual definition of real world evidence. The FDA’s draft guidance capitalizes the term and lists specific examples, but that list is expected to grow, Patel, said.
“How are you using 'real world' as a measurement? We have expanded it at the FDA to include patient registries, data from devices, EHRs; it is much broader than you may be thinking,” Patel said. “Being here at Connected Health and talking about patient data … that doesn’t necessarily mean that it’s all out there. Dexcom has big data. Gail has lots of data, but all that contextual data is lacking. How do you bring that all together? You can have the smartest algorithms in the world but you don’t have the foundational things necessary to make decisions.”