Digital Twins: Will The Doubling Assist Personalize Healthcare?
TThe frequently cited new trends in healthcare are personalized care and remote care. At first glance, it might sound as if they were contradicting one another: How can care be made even more individual if you spend less time with your caregivers? The answer to this question could lie in the use of digital twins – virtual models of people that could revolutionize multiple facets of healthcare.
Twins like this aren’t exactly new. NASA pioneered the concept in the 1960s. In the early 2000s, the approach had gained acceptance in manufacturing, although the term “digital twin” was only introduced about a decade ago.
Digital twins are virtual representations of an object or system over its life cycle. Their development requires a thorough understanding and representation of the parts within a system and their relationships to one another, as well as the analytical ability to evaluate the effects of the variables introduced into the system.
Possible uses of digital twins include predicting the future of a real system or object and optimizing its maintenance. Experiments carried out with them can also provide insights into how the real system or object can react to changes in its ecosystem.
The construction, automotive and aerospace industries, among others, have implemented the use of digital twins to manage, evaluate and improve systems. But they are now increasingly being applied to humans, opening up exciting new use cases in areas like healthcare.
How digital twins could revolutionize healthcare
Creating customized full-body models is the holy grail for digital twins in healthcare. This innovation could transform healthcare at the patient, provider and institutional levels, and even have an impact on research and development.
Individuals could feed their digital twins with diverse and real-time information obtained from wearables as well as other sources of self-reported data collected outside of the healthcare system. Healthcare providers who access a patient’s digital twin would then have personalized information that goes well beyond what is currently available to them to make decisions and recommendations.
The implications of digital twins in healthcare are innumerable. Some potentially breakthrough applications are:
Understand individual risk factors. A digital twin could contain a person’s full genetic profile as well as personalized environmental and behavioral information to better predict, prevent, and prepare for future health conditions.
More accurate and faster diagnosis. A person’s digital twin could synthesize data from imaging records, laboratory results, genetic information, and body measurements to improve the diagnosis of detected and previously unidentified diseases.
Predicting responses to interventions. When prescribing medication, a digital twin could consider a person’s pharmacogenomic data to suggest optimal therapy. In the event of surgery, digital twins could be used to simulate procedures, not only to predict outcomes, but also to identify the optimal devices and techniques for a procedure.
Fewer, faster, and safer clinical trials. Trials testing experimental drugs on digital cohorts of real patients could accelerate clinical research and reduce the number of expensive trials required for new therapies to be approved. This would also make it possible to scale studies on a large scale to different virtual patient populations. In both the United States and Europe, regulators are being urged to allow a wider use of modeling and simulation within the regulatory process.
Optimize healthcare facilities. Digital twins of hospitals or treatment facilities can predict crises, improve safety and performance, and identify cost or waste drivers. Applications can range from predicting bed shortages to managing patient flow and reducing the spread of pathogens. It would also be possible to run digital stress tests to see how the institute behaves in extreme conditions such as a pandemic.
When do I meet my digital twin?
Whole human digital twins have not yet been developed, but efforts are being made. To achieve this goal, Q Bio integrates full-body scanning with a digital twin model. Digital twins are now in use for individual organs. Dassault’s Living Heart project is a realistic virtual model of a human heart. Digital twins are even being used to treat Covid-19 long-haul vehicles as Dell and the i2b2 tranSMART Foundation work to create digital twins of long-haul vehicles on which researchers will run millions of individualized treatment simulations.
Me, my doctor and virtual me
The introduction of whole human digital twins will not only change the possibilities of medicine, but will also affect doctor and patient behavior as well as the doctor-patient relationship. Digital twins and other data-rich innovations require physicians to have better knowledge of data science and how to manage and troubleshoot complex systems with disparate inputs. For patients, the introduction of these technologies will lead to more frequent and specific health feedback. However, many of these new decision points likely relate to lifestyle management, not medication or procedural interventions.
In terms of the doctor-patient relationship, digital twin functions will support the further rise of trends such as remote patient care and telemedicine, which have been accelerated by the Covid-19 pandemic but cannot yet be fully scaled. As a digital twin, a person can exist in the cloud, which increases the possibility of accessing the supply from anywhere. This could manifest itself in greater choice for healthcare consumers, removing some of the constraints imposed by local universal healthcare and giving providers access to individualized health data on which to base decisions.
The tradeoff for this will mostly come in the form of less personal contacts and interpersonal relationships in healthcare, making the future of healthcare more personal but less personal.
While the future is bright for digital health twins, the real impact of technology will ultimately depend on its ability to translate information into reliable, large-scale clinical advice. To support this transformation, better data, new patient-provider relationships, and the regulatory rubric are needed to deliver on these promises.
Ben Alsdurf is the US Health Care Practice Lead for TLGG Consulting, a New York and Berlin-based digital management consultancy that advises clients on corporate strategy and business model design.