How Digital-Twin Technology Can Transform the Healthcare Industry

Researchers use digital twins to test scenarios safely and cost-effectively before attempting them in real-life situations or environments.

FREMONT, CA: It would be prohibitively expensive to assemble cohorts of comparable patients in real life. The technology also avoids using real-life subjects, which come with their own set of risks and consent issues. When used correctly, digital twin technology in the healthcare sector allows clinicians to determine optimal therapies, improve patient outcomes, and maximize efficiency, resulting in hospital cost savings.

Simply put, digital twins provide a safe and secure environment for testing the impact of change and will be critical to many sectors as one works to recover from the pandemic's effects.

Digital-twin technology allows for "patient like me" comparisons across large cohorts of medical twins. This can aid in identifying biological markers for diseases and compare and test treatment options for patients who share similar characteristics such as age, gender, ethnicity, and even underlying conditions. However, such an analysis would be impossible for medical professionals to perform on a large scale with real-world patients.

Digital-twin technology has the potential to transform clinical research methodology. Users can use the technology to ask better questions, get better answers, and derive actionable insights without endangering the health of real-life subjects. Using digital twins, of course, necessitates a data-first mindset. More data means more digital twins, more discoveries, and better treatment.

Research collaborations have begun using digital twins to assist the millions of people affected by the pandemic. According to estimates, one in every twenty people infected with the virus will experience Covid-19 symptoms lasting more than eight weeks. In addition, some people are still suffering from debilitating symptoms months later.

Many people are too sick to tolerate experimental treatment regimens, and it remains unclear why some people recover quickly while others suffer long-term consequences. Digital twins have the potential to solve the mysteries of this previously unknown condition, providing answers to key questions such as: who is most at risk and why, what are the most common symptoms, and what treatments are most effective?

The key to unlocking the potential of medicine is data. And, with the introduction of "big" health data – electronic health records, digitized medical images, genome sequencing – we are all candidates for digital twinning.