From pocket-sized electrocardiograms to watches that measure blood glucose levels, the field of medical technology is rapidly evolving. But these innovations, though oftentimes successful, aren’t necessarily living up to what scientists want them to be, according to presenters at the American Heart Association’s Scientific Sessions 2017.
A group of doctors who deal with technology and innovation in the medical landscape spoke to both the strengths and weaknesses of device development in a field that’s more demanding of its researchers and engineers than ever before, expanding on ways we can eliminate current barriers and advance medical technologies to better serve patient—and provider—populations.
These are five takeaways:
1. Scientists should be assembling more diverse development teams.
Most researchers working on new medical technology are often field-specialized and tech-savvy, Stanford University’s Paul J. Wang, MD, said at the conference. While this is both helpful and necessary, limiting a design team to individuals of similar expertise could be a mistake.
“It’s difficult to introduce new technology,” he said. “Who invents? Do clinicians invent?”
The answer to that question is overwhelmingly “no,” he said. While a fraction of scientists on these projects are clinicians, Wang said he thinks it’s necessary to include doctors and engineers from other disciplines in the innovation process.
Not enough people are working on medical technology solutions, he said, and those who are find themselves at a loss for adequate funding. He said he believes building a community of innovators through offering specialized training paths, seed funding and competitions could be the first step to better collaboration.
2. We need to bridge the Valley of Death.
Scientists attempting to contribute to medicine’s new technological landscape will likely find themselves at an impasse—a gap in their road to innovation known as the “Valley of Death.” While conceptual research is often generously funded—mostly by the National Institutes of Health, Wang said—there remains a chasm between that initial stage and FDA approval, clinical development and the launch of a device.
Wang said this results from a lack of consistent funding during the prototype design and preclinical stages of development, which are hugely important but remain unfunded by national groups like NIH or Big Pharma.
Finding good money flow for early stages of device development is a “monumental task, and has become a lot more difficult recently,” Wang said.
He said advising and assisting researchers in learning about funding sources, working with industry and grant providers to create funding paths and creating incubators could all be shifts the industry could make if it wants to continue to fuel innovation.
3. Patients want to self-monitor with wearable technology.
Smartwatches, activity monitors and FitBits are just a handful of devices that have been introduced to a consumer market that’s increasingly demanding at-home, on-the-go medical monitoring. These high-grade, wearable technologies have opened a world of new treatment possibilities for doctors, but they aren’t without their limitations.
Watches can now monitor pulse, blood pressure, temperature, activity, hydration status, sleep stages, stress and even blood glucose levels, Jagmeet Singh, MD, PhD, said, and those features are helpful for remote management of conditions like atrial fibrillation. For example, Singh said, AliveCor’s Kardia—a portable EKG—allows him to monitor his Romanian patient’s AF from Boston.
“These are devices that connect us with our patients, and connect their lives with us,” he said.
Wearables also have the ability, through various sensors, to collect information that could raise red flags for physicians and prevent future heart failure hospitalizations in patients.
However, he said, there’s currently a shift in how remote monitoring works–while information from wearable devices has in the past been documented and sent straight to a patient’s doctor, those numbers are increasingly being passed on to lower-level physicians and assistants. The goal, he said, is that numbers collected from wearable devices will be transmitted to smartphones, where patients themselves will be able to view the statistics and intervene on their own.
To make the most of wearables, Singh said, we need to create more and better sensors, generate more accurate data, build reliability and change the culture of care.
4. Doctors need to adopt a continuous care model rather than the current episodic routine.
Healthcare right now is episodic and transactional, Singh said, which isn’t ideal. A continuous care model would be more preventative, allowing doctors to monitor patients constantly through wearable or implantable devices and intervene appropriately instead of waiting for a three- or six-month checkup.
We need to digitize humans like we do cars, Singh said—they have check engine lights and alert us when they need an oil change. With implantable devices, scientists can monitor a patient’s body more closely than ever before.
“Every organ system has the ability to be digitized,” he said. “There are sensors that can be implanted with every organ.”
We’ve progressed as a scientific community from basic heart monitors to devices that can monitor heart sounds, chemicals and biomarkers, he said. But to use these technologies successfully, there needs to be a shift in how doctors approach caring for their patients.
5. There needs to be a stronger push for user retention solutions.
Ever bought a FitBit or other activity monitor? Singh is willing to bet that if you have, it saw religious use for a few weeks before being tossed in a drawer. With wearable technology, he said—no matter how popular the device—user retention tends to be low.
“User retention is a big problem when we try to connect lives with devices,” he said. “We have to create a value-based care model, but we have to provide incentivization for both the provider and the patient to use this information in making sure they’re providing the best care.”
More affordable payment models could help, he said, as well as further deep learning for actionable intelligence.
“That’s going to require some major training and behavioral economics in certain trials and studies to make sure that these changes can be implemented and reproducible and last,” he said.