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Two often-cited emerging trends in the health care space are personalized care and remote care. At first glance, these may sound like they are at odds with each other: How can care become even more individually tailored, if you’ll be spending less time with your care providers? The answer to that question may lie in the use of digital twins — virtual models of individuals that could revolutionize multiple facets of health care.

Twins like these aren’t exactly new. NASA pioneered the concept in the 1960s. By the early 2000s, the approach had taken hold in the manufacturing sector, though the term “digital twin” was introduced only about a decade ago.

Digital twins are virtual representations of an object or system across its life cycle. Developing them requires an accurate understanding and representation of the parts within a system and their relationships to each other, as well as the analytic power to evaluate the effect of variables introduced to the system.

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Uses of digital twins include making predictions about the future of a real-life system or object and optimizing its maintenance. Experiments conducted with them can also offer insights as to how the real-life system or object may respond 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 people, opening up exciting new use cases in fields like health care.

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How digital twins could revolutionize health care

Creating individualized, whole-body models is the holy grail for digital twins in the context of health care. That innovation could transform health care on the patient, provider, and institutional levels, and even have implications for research and development.

Individuals would be able to feed their digital twins with diverse and real-time information obtained by wearables, as well as from other sources of self-reported data collected outside of the health system. Care providers accessing a patient’s digital twin would then have personalized information far beyond what is currently available to them on which to make decisions and recommendations.

The implications of digital twins in the health space are innumerable. Some potentially game-changing applications include:

Understanding individualized risk factors. A digital twin could incorporate an individual’s complete genetic profile, as well as personalized environmental and behavioral information, to better predict, prevent, and prepare for future health conditions.

More accurate and rapid diagnosis. An individual’s digital twin could synthesize data from imaging records, lab results, genetic information, and body measurements to improve diagnosis of detected and previously unidentified maladies.

Predicting responses to interventions. In the case of prescribing drugs, a digital twin could incorporate an individual’s pharmacogenomic data to suggest an optimal therapy. In the case of surgery, digital twins could be used to simulate procedures not only to predict outcomes but 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 to approve new therapies. This would also allow studies to be greatly scaled across diverse virtual patient populations. In both the United States and Europe, regulatory agencies are being urged to allow wider use of modeling and simulation within the regulatory process.

Optimizing health care institutions. Digital twins of hospitals or treatment facilities can predict crises, improve safety and performance, and identify drivers of cost or wastage. Applications can range from predicting bed shortages to managing patient flow and reducing the spread of pathogens. It would also make it possible to perform digital stress tests to see how the institution would perform under extreme conditions, such as a pandemic.

When will I meet my digital twin?

Digital twins for entire human beings have not yet been developed, but efforts are underway. Q Bio is integrating full-body scanning with a digital twin model in pursuit of this goal. In the meantime, digital twins for individual organs are in use. Dassault’s Living Heart project is a realistic virtual model of a human heart. Digital twins are even being tapped to help treat Covid-19 long-haulers, as Dell and i2b2 tranSMART Foundation are working to create digital twins of long-haulers on which researchers will perform millions of individualized treatment simulations.

Me, my doctor, and virtual me

In addition to changing the capabilities of medicine, the introduction of whole human digital twins will affect doctor and patient behaviors as well as the doctor-patient relationship. Digital twins and other data-rich innovations will require doctors to be more proficient in data science and managing and troubleshooting complex systems with diverse inputs. For patients, adoption of these technologies will result in more frequent and specific feedback on health. Many of these new decision points, however, are likely to be about lifestyle management, not medications or procedural interventions.

In terms of the doctor-patient relationship, digital twin capabilities will support the further ascent of trends like remote patient care and telemedicine, which were accelerated as a result of the Covid-19 pandemic but aren’t yet ready to be fully scaled. As a digital twin, an individual can exist in the cloud, meaning increased ability to access care from anywhere. This could manifest as more choice on the health care consumer end, lifting some of the limitations imposed by localized general health care and giving care providers access to more individualized health data from which to make decisions.

The trade-off for that will come largely in the form of diminished face time and interpersonal relationships around health care, rendering the future of health care more personalized but less personal.

Although the future for digital twins in health care is bright, the true impact of the technology will ultimately depend on its ability to translate information into reliable clinical advice at scale. Supporting this transformation will require better data, new relationships between patients and providers, and the regulatory rubric to validate these promises.

Ben Alsdurf is the U.S. health care practice lead for TLGG Consulting, a New York- and Berlin-based digital business consultancy that advises clients on corporate strategy and business model design.

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