Kaushik Gune

Advancing Digital Health: Huma SaMD Platform Gains U.S. Clearance

By MedTech Intelligence Staff
Kaushik Gune

This week Huma Therapeutics received FDA Class II 510(k) clearance for its Software as a Medical Device (SaMD) platform, potentially speeding approval of a variety of AI and machine-learning (ML)-powered digital health devices. We spoke with Kaushik Gune, U.S. Head of Healthcare at Huma, about the current state of digital health technologies, the value of partnerships to enhance the use of real world data and how digital health is likely to advance in the coming years.

This week Huma Therapeutics received 510(k) clearance for its configurable Software as a Medical Device (SaMD) disease management platform, which previously received Class IIb approval in the EU. The FDA Class II clearance allows users to add AI algorithms to the disease-agnostic platform, potentially speeding approval of a variety of AI and machine-learning (ML)-powered digital health devices.

We spoke with Kaushik Gune, U.S. Head of Healthcare at Huma, about the current state of digital health technologies, the value of partnerships to enhance the use of real world data and how digital health is likely to advance in the coming years.

Can you tell me a little bit about the Huma Platform and how it is utilized?

Gune: We started about 12 years ago when digital health was considered a gimmick; today it’s part of our everyday life. We started with remote patient monitoring in rare diseases. Each rare disease is so different that it forced us to evolve into a very modular platform that can not only take care of one rare disease but a second and a third and so on. As a result, we have now expanded to include 20-plus diseases. We can serve, for example a cardiology patient as well as an asthma patient as well as a COPD patient.

Along the way, we have joined forces with other companies that are doing some amazing work. In 2022 we acquired a company called iPlato, which is our primary care prevention app, and late last year we acquired Alcedis, a clinical trial company that has over 25 years’ experience in running clinical trials. With all of these assets together, we have technology that can follow the patient from the healthy patient engaging with our platforms for screening and checkups to disease onset through remote monitoring, as well as monitoring patients participating in clinical trials.

When you have all this data from a lot of different disease states, what is required to sort and manage that data so when you do want to use it you know you’re pulling from a uniform area of the disease state?

Gune: There are two pieces to that. One is data privacy and security, that’s the safety element. We have invested in resources, capabilities, infrastructure and internal processes to make sure that we have the best knowledge on data privacy and security. We’ve also invested in external certifications, including ISO certification, to make sure that we are on track with the latest developments.

The second piece is, if you are going to run any analytics, AI or machine learning using this data, the most important question is, is your platform regulated? In a non-regulated platform you can use that data to provide a LAG type of system to the clinicians. But, if you are going to provide insights based on personalized data of that patient and of that patient’s surroundings and make either treatment recommendations or a predictive forecast about the patient’s condition, you need a regulated platform.

When we look at regulatory approval surrounding the use of real world data for clinical trials or devices that provide treatment recommendations, is there more clarity in the U.S. now or are there still hurdles in terms of understanding how you can integrate that?

Gune: When you think about clinical trials, the whole world is still grappling with that. Real world data standards are used more for informing future research and development in pharma and MedTech, and less for efficacy. We still need clinical trial data to determine safety and efficacy.

A recent report released in the UK talked about how the UK is falling behind in clinical trial innovation. Huma is cited in that report as one of the innovators in clinical trials. In partnership with AstraZeneca, we ran Covid-19 vaccine trials, in which we recruited about 100,000 patients across the globe, using a mobile app to report data without ever visiting a trial center. In Germany we’re working with AstraZeneca to collect real world data on COPD, and we were able to recommend about 7,000 patients in a month. That’s the innovation that is required to bring health equity—or research equity as we call it—across the globe. And you are doing that at speed.

What have you learned about people’s openness toward partnering with other companies to share data, and what are some of the challenges or realities of business that you need to be aware of when trying to forge those partnerships?

Gune: From one angle I understand that we could collaborate 100x more if we didn’t have these closed doors. But there is confusion about who owns the data, and it is a rightful confusion. The simplistic answer is the patient owns the data, but the clinicians and providers have spent a lot of energy and time collecting that data and managing that data, so I can see how physicians feel that they have some rights over that data as well.

But if you dig a little bit deeper, what patients are most concerned with when they talk about data is, is my data going to be used to give me better medicine or potentially create better medicine for future generations? Virtually every person on the planet would give a thumbs up for that. What they are really scared about is, will my data be used to deny access to treatment or insurance coverage as a precondition, that is a big fear. Same thing on the provider side. What they fear is, we are the front line of health care, is the data that we generate and collect going to be used by a company to make millions or billions of dollars?

Partnerships are at the core of Huma, and these partnerships span pharma companies, pharma distributors, and healthcare providers. What we focus on is making sure our partner’s interests are protected when it comes to data and the potential value we can create from that data. If we are up front with them, then we see a lot more collaboration.

How is Huma currently using AI and machine learning?

Gune: We’re using it in multiple ways. In the respiratory space, we are looking at how to drive more predictive signaling based on patient data as well as geographical and environmental data to predict a risk for an exasperation of an asthma attack. We also have developed a cardiovascular risk score that assesses your risk for cardiovascular disease over the next 5 to 10 years, and it is based on data from the UK Biobank, and right now we are working in collaboration with Bayer to screen patients through a simple app.

But we see applications of AI on all our platforms, including applications related to the patient experience, such as sending personalized content to specific patients using AI. I think the applications of AI are endless.

Looking at digital health in general, what do you think is the next step in terms of improving digital health or improving adoption of digital health to enhance care delivery and patient health?

Gune: I see three avenues for digital health. One is scaling. In Europe, we are at scale with about 1.8 million active users and we are deployed in over 3,000 hospitals and clinics, and our goal is to bring that to the U.S. over the next few years. I expect a few companies to achieve that million-patient type of scale soon.

Next is incorporating AI to drive more penetration and more support for healthcare workers and bring more value to patients. The third piece is continuing innovation of new delivery of care models. That will require a lot of support from CMS and various reimbursement parties across the globe, as well as more participation from pharma and MedTech companies. For example, there’s a lot of potential for digital health in surgery, and those models are still not explored. I expect to see more models coming out with the potential for scale in the surgery area.

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