Six minutes into Apollo 13’s mission to the moon In 1970, its oxygen tank exploded. The event prompted NASA to develop a brand new approach to predicting possible failures in its spacecraft. The approach relied on continuous sensor data, which then fed deep digital simulations, enabling far more rigorous testing of complex spacefaring systems. It was the very first use of “digital twin” technology.
Today, digital twin systems are used across industries to enhance operations and accurately simulate any change in a system. Tech firms like Apple and Tesla use digital twins to observe product performance in the sphere and determine whether or not specific system components require maintenance.
Digital twins have also been utilized in healthcare, albeit largely in drug research and development. Its biggest potential, nevertheless, is in chronic disease management. By coupling machine learning and Web of Things technology with digital twin AI, an approach that originated with something as vast as space exploration has the potential to make healthcare truly individualized.
Digitizing traditional care has failed
Modern medicine has made incremental moves toward personalized care over the past decade by giving patients a voice in decision-making, and toward precision medicine through advances in genomic research. Each have helped tailor care to the person, but for essentially the most part, our healthcare system takes a “large group” approach to care delivery.
It’s evident in the way in which we manage chronic disease. Every certainly one of the 133 million Americans currently living with a number of chronic diseases is ready upon a planned care pathway – a treatment regimen, a fad weight-reduction plan, often numerous medications – and their improvement is measured in batches of 1000’s of other individuals who share their condition.
This approach hasn’t worked. Notoriously, U.S. spending on diabetes, heart disease, and cancer continues to rise, and technology’s impact on outcomes and costs has been limited. In digital management of diabetes, weight reduction, and other conditions, that impact has been a non-factor.
In March, a report published by Peterson Health Technology Institute underlined this lack of sustained results. The report found that every one of the evaluated solutions perform poorly on engagement and outcomes over time. Because of this, weight reduction, A1C reduction, medication elimination, diabetes reversal, and the health, well-being, and economic advantages of those solutions are each limited and unsustainable.
That’s because most solutions just digitize an ineffective template for care. They don’t account for individual differences. Every body brings their very own set of cultural, biological, dietary, behavioral, and environmental aspects that influence their health at a deeply individual level.
Moving from ‘personalized’ care to individualized care
Digital twin AI guarantees a departure from the template. Core to the technology is the concept that each individual is an N of 1. A person’s digital twin is informed by a continuous measure of their unique clinical and behavioral variables, and uses that data to shape care guidance toward the very best and healthiest version of that individual.
The ability of digital twin technology is in its attention to the small things – the things we eat and do – and the way they impact our current and future selves. In practice, digital twins can accurately predict the effect a steak dinner may have on a selected person’s metabolic or cardiovascular health. To the extent that impact could also be negative, digital twins can offer ways to mitigate the repercussions. It would suggest a 10-minute walk or another dessert. As a substitute of ice cream, perhaps it’s banana nut bread with Greek yogurt and fresh berries or just a unique sequence.
In this manner, digital twin AI can show a person what’s in store for them in the event that they stay on their current trajectory and the large changes that may occur by making small adjustments over time. Sustain your current routine, and also you’ll find a way to stop taking metformin in three weeks. Fall back into old habits, and you possibly can expect to select up a refill.
It’s potent technology, and while its impact on healthcare has largely been recognized only in academia, it’s starting to search out its role in business use cases. In 2014, Dassault Systemes and the FDA launched SIMULIA Living Heart, a project that works with device manufacturers to develop and refine cardiac devices at a faster pace. On the onset of the pandemic, OnScale’s Project BreathEasy developed a digital twin of the lungs of COVID-19 patients to enhance and optimize the usage of ventilator resources.
Medical researchers are also using digital twin disease models to predict the effectiveness of pharmaceutical interventions based on complex, extremely individual biological processes. Takeda Pharmaceuticals has embraced the technology to shorten pharmaceutical processes and make realistic input-output predictions for biochemical reactions. More recently, researchers used digital twin technology to simulate therapy outcomes and determine the very best treatment for oropharyngeal carcinoma based on the person.
Chronic disease management is the following frontier
A recent paper published in Nature asserts that digital twins are “poised to make substantial contributions” to cancer care, especially in monitoring the progression of the disease and evaluating treatment responses, which infamously vary individual by individual. The identical paper analyzes cardiac digital twins fed by imaging, EHR, genetic, and continuous wearable data, and their potential to predict acute cardiac events.
These advancements will give option to life-changing healthcare technologies. Their power lies in an idea core to their purpose: nothing complex is static.
This is very true of our biological systems. A digital twin requires 1000’s of information points per day, per individual, to really understand the interplay between a person’s biology, culture, lifestyle, preferences, and health. A few of this data is already being captured by wearables and mobile apps, but and not using a model that puts that data into the context of the person and their care journey, it’s rudderless.
On this planet of chronic disease management, the small things can in a short time develop into big, life-threatening things. And while digital health has raised the hopes of patients with language like “personalization,” the tools and approaches which have been offered to people haven’t addressed their unique needs and preferences.
Digital twin AI will turn this approach on its head by helping us higher understand and improve our health on a deeply personalized level. It’s a technology poised to meet the promise of individualized care.
