The Dawn of a New Era of Digital Twin Diagnostics
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The following article is an opinion piece written by Benedikt von Thuengen. The views and opinions expressed in this article are those of the author and do not necessarily reflect the official position of Technology Networks.
You can't put a price on your health. But you can certainly add up many of the costs of sickness – ranging from the personal lifestyle effects of pain and suffering to the societal costs of sickness benefits and healthcare systems at breaking point.
It's tempting to think the answer lies in developing increasingly complex treatments. But we cannot outrun basic human demographic trends such as aging populations, higher disease burdens and a growing global middle class. Access to healthcare is already unfair – so imagine what it will be like in 10–20 years' time. Something has to change.
The future of healthcare needs to be built on early detection of disease – ideally even before any symptoms appear. But that's easier said than done. How do you know which tests to do – and when? Regular thorough check-ups are only feasible for wealthy individuals who can afford to pay. And, even then, a disease could develop for months unnoticed in between check-ups.
The answer could lie in digital twin technology. If we each had a virtual representation of ourselves, including all our organs, in the form of a digital human twin, that virtual “being” could be constantly monitored to detect the very earliest signs of disease and automatically alert the healthcare system – stopping most diseases in their tracks.
Digital twins have already been widely deployed in manufacturing for continuous monitoring of equipment and simulation of various scenarios, such as wear and tear or different environments. Complete digital twins of aeroplane turbines, for example, are continuously fed with real-time sensory data from the actual turbines. This enables much better maintenance planning and can even predict when a turbine is likely to break down.
Now attention is turning to the use of digital twin technology in healthcare. It holds the promise of making it easier to personalize medical treatment based on factors such as an individual's unique genetic make-up, anatomy and behaviour. The technology could also unlock more accurate monitoring of medical devices – and even model healthcare organizations to improve the way they deliver care.
But a crucial step is to move towards detecting diseases as early as possible – by building a digital human twin. This needs a wide range of sensors to capture a variety of data points such as digital (sound, image, etc.), biophysical (blood, saliva, etc.) and behavioural (emotional state, language, etc.). These data points then need to be aggregated into a dynamic digital human twin that can monitor your health and alert the healthcare system as soon as something changes.
Instead of having to regularly undergo an expensive battery of tests to make sure you are healthy, you would have a small, tailored set of low-resolution, ultra-cheap tests running in the background on your digital twin. The technology would make it possible to select the right combination of diagnostic tests for you – at the right time – based on your genes and underlying state of health. These background tests would then continuously monitor your health baseline. If any small change was detected, this would trigger additional, more specific tests to help a healthcare professional accurately identify what might be going wrong – and intervene at the earliest possible time.
Digital human twins may sound like science fiction – but we actually already have all the right ingredients today.
We are seeing an explosion of all types of data, for example, the rise of digital health tools and consumer biotech, cheap computing power and some incredible advancements in machine learning. Regulators are also taking note of these trends and increasingly pushing for real-world data and endorsing software solutions. The International Medical Device Regulators Forum, for example, has already created a regulatory framework for software as a medical device. Most importantly, though, people want better healthcare solutions and are increasingly taking management of their health into their own hands.
So we have all the ingredients, it's now a question of putting them together to build a dynamic digital human twin. A number of companies are already working on various aspects of this brave new world – with Hewlett Packard Enterprise creating digital models of the brain, for example, and Philips modeling the heart.
Of course, any digital human twin will need to be clinically validated before it can be rolled out to healthcare systems. Proving clinical usefulness will also help drive reimbursement, as early detection is almost always cheaper than treating a late-stage disease.
The stakes are high – early detection of disease has the potential to save countless lives and dramatically reduce healthcare costs. And digital human twins will help us all worry less about the most important thing in our lives – our own health and that of our loved ones.
The right approach to digital human twins will also make healthcare more accessible to everyone. Growing global adoption of smartphones will allow us to give billions of people free access to low-resolution digital human twins that will monitor their health continuously. When their health starts to change, people can then be funnelled into the relevant healthcare system and/or receive higher-resolution combination tests recommended by the digital human twin and paid for by their healthcare system.
Other benefits of digital human twins include the ability to simulate how people are going to respond to certain treatments – helping with drug development. They will also provide new insights into human biology and, through testing different biomarkers, we will be able to increase our understanding, which will also help with drug development as well as treatment.
All in all, we can look forward to the dawn of a new era of digital twin diagnostics, which is set to improve the health of all of us.