From Patent To Product: The Speed Of The Digital Health Evolution

We’re bombarded with mindblowing headlines of new medical miracles every day. BCI helps paralysed patients talk again! Robots in the stomach! Micro-organs on organ-on-chip technologies! But it is almost impossible to see through the hype and know if and when these will yield actual, patient-ready solutions. So let’s get into this maze and decipher how a new, revolutionary medical technology develops from an ingenious idea to a market-ready product with two real-life examples: the artificial pancreas and wireless ECG.

In early April, the UK’s NHS rolled out an artificial pancreas (APS) for Type 1 diabetes patients, as a world first. This is a still-not-final chapter in a story that started in the 1960s. What the heck takes so long when we need to save lives? Why do medical inventions take decades to hit the market? And how could we speed up the process and still stay safe??

The triumphant headlines about digital health and AI unicorns might tempt us to believe that a brilliant idea and a dash of entrepreneurial magic are all it takes and we are set for life. But revolutionary healthcare products are rarely born overnight, the journey is marked by obligatory milestones of research and regulatory navigation.??

It took over two generations to help diabetes patients

The artificial pancreas story is decades older than me, a perfect case study for two reasons. First, it took over 60 years to bring the concept to life. Second, this starkly illustrates the chasm between patients' urgent need for affordable solutions and the slow-moving gears (and sometimes totally different focus) of research, science, and industry.

• 1960s: The concept of an artificial pancreas began with research into continuous glucose monitoring (CGM) and insulin pump technology.
• The mid-70s: Biostator, a "glucose-controlled insulin infusion system" was patented. It could measure blood glucose levels and inject appropriate doses of insulin but could be only used in hospitals.
• 1970s-1980s: Early prototypes of closed-loop systems were developed, combining glucose sensors and insulin pumps to test the feasibility of automating insulin delivery.
• 1990s: The first in-patient trials of closed-loop systems.
• 2013: The first hybrid closed-loop system, Medtronic's MiniMed 530G, received FDA approval. It required manual meal announcements but automated basal insulin delivery overnight.?
• 2015: Tired of waiting and angry for the stellar early device prices, the Open APS movement published material on how to build your hybrid closed-loop system at home. As of today, thousands of diabetes patients use DIY devices based on the open-source knowledge collected by these patient scholars.
• 2020: Tandem Diabetes Care's Control-IQ technology, an advanced hybrid closed-loop system that adjusts insulin delivery based on CGM data, received FDA approval. It offered features like automatic correction boluses.
• 2024: UK’s NHS announces that it starts rolling out APS for diabetes patients, the process is expected to require another 5 years.

No wonder diabetes patients have been eagerly awaiting breakthrough solutions: nocturnal hypoglycemia can be life-threatening. Not to mention the many long-term complications of diabetes, which are also not a walk in the park and can severely impact quality of life. But they had to wait two generations to finally get close to a widely available, affordable device.?

Based on the NHS prediction of needing another 5 years or so till APS becomes the norm - and this is just the UK, many countries lag way behind - 15 years needed to pass after the #Wearenotwaiting patients created functioning DIY artificial pancreas. Over 30 years from the first clinical trials of closed-loop systems. The artificial pancreas technology required over 60 years, two full generations of humans to develop from concept to a hopefully soon-widespread product, of which 50 years have passed since the first patents were registered.

Smartphone-connected ECG: From Patent to Product

Compared to the APS story, wireless ECG devices from Kardia (formerly AliveCor) were developed in the blink of an eye. This is a good case study for another reason: it shows that it is possible to stick to the principles of evidence-based medicine and create a revolutionary product.

Developers of this technology understood that they needed to overcome the initial reluctance from medical professionals to use it. And this won’t be possible without meeting the standards and requirements of medical technologies.

• The company first made an FDA-approved smartphone case that worked as a single lead ECG in 2012. They launched two clinical trials to test the hardware and the app comparing it to a traditional 12-lead device.?
• Later, the evolution of its design resulted in a credit-card-sized device and an even smaller version in 2019. The original device could provide a one-channel ECG by playing the user’s fingertips on the sensor for 30?seconds. The results were uploaded to the cloud to make it accessible for physicians.?
• In 2015, Alivecor received FDA clearance to use an algorithm for the analysis of the readings to determine issues related to heart rhythm without human help.
• By the end of 2017, they already used deep learning networks, and the FDA cleared the company’s ECG reader called KardiaBand as a medical device accessory to the Apple Watch. A study concluded that the device managed to distinguish between atrial fibrillation and a normal heart rhythm with a sensitivity of 93, and a specificity of 94%, respectively. Its sensitivity increased to 99% when a medical professional reviewed the reading.
• By 2020, products of Alivecor have been tested in over 40 clinical studies. Despite these accomplishments, the use of the device is still not common practice. And as other companies producing AI-based medical technologies are lagging, it might depict a long period of adoption.

We can speed up innovation and stay safe?

Medicine and the regulation of new medical technologies have their rules for a reason. We, at The Medical Futurist, are huge fans of staying evidence-based and backing new methods and devices with double-blind trials and peer-reviewed studies. Info on ongoing clinical trials should not be published on social media, even if you are a billionaire.?

However, there are lessons to be learned from past stories, and these could help us create frameworks that could shorten the time needed for life-changing medical technologies to reach patients.?

1. Regulators can/must think ahead

Groundbreaking medical innovations never surface as complete surprises: there are patents (usually dozens of patents), proof-of-concept trials marking the way. By analysing patent submissions, regulators can understand what directions science and technology are taking, and think ahead of what these mean from the regulatory point of view.?

An example is how The Medical Futurist Institute published a systematic analysis forecasting AI trends in healthcare based on patent submissions. By looking at medical and healthcare-related AI and ML patent trends, regulators and policymakers could better determine medical specialties, technological trends, or areas such as imaging to dedicate more attention to. Thus, when a range of AI- and ML-based technologies become available in those fields, proper regulations will ensure a safe and efficient implementation into the practice of medicine and the delivery of health care.

1. Well-designed national frameworks do wonders

Germany’s digital health application (DiGA) system is a good example of helping innovation meet safety. They created a fast-track model that significantly reduces the time to market, without compromising patient safety.?

Traditionally, only larger MedTech or pharma companies have the resources to pursue years of clinical trials and navigate the bureaucracy behind the certification processes. And these large-size corporates are not known for fast innovation. The DiGA system was designed to help smaller enterprises succeed, not only by simplifying the administrative route to market but also by statutory health insurers reimbursing the associated DiGA costs, with prices negotiated in advance with the umbrella association of German health insurance companies.

1. Patient design, patient design, patient design

We can’t repeat it often enough: patients know best what patients need. We can bet that the artificial pancreas would not have needed 60+ years if the R&D process included actual patients on the decision-making levels. We need to remember that medicine and medical research should primarily serve patients. Researchers need to change priorities to match patients’ urgent needs.?

This was painfully accurately said when a father of a son with suicide ideation told Dr Thomas Insel after a speech, “Our house is on fire, and you’re telling us what you learned about the chemistry of the paint.” The scientific literature may contain volumes about “the paint,” but Insel realized “this gap between our scientific progress and our public health failure.” He left academia to pursue product development to solve real-world problems.