Adopting Digital Twins in Healthcare for Better Patient Outcomes

The history of digital twins started in the 1960s with the Apollo 13 mission. When disaster struck with an oxygen tank explosion, NASA engineers used multiple simulators to troubleshoot mission-critical components and resolve problems, safely bringing astronauts back home. Fast forward to 1991, Michael Grieves first introduced the idea of applying the digital twin technology in the manufacturing sector. But it wasn't until 2010 that the term “digital twin” was officially coined by John Vickers from NASA. Since then the digital twin concept has significantly evolved, extending beyond manufacturing. And one of the industries where its impact shines the most is healthcare. With the potential to transform patient care, improve diagnostic accuracy, and slash healthcare costs, digital twins can reshape how we approach health and well-being. In fact, analysts predict that the market for digital twins in healthcare could skyrocket to a staggering $33.4 billion by 2035. Bar chart showing the projected growth of digital twins in healthcare from 2024 to 2035, divided into whole body and body part twins, highlighting a significant increase in market value.

Bar chart showing the projected growth of digital twins in healthcare from 2024 to 2035, divided into whole body and body part twins, highlighting a significant increase in market value.

Source: Rootsanalysis

Exploring digital twins in healthcare

Fundamentally, a digital twin is a digital version of a tangible system or process, brought to life by interconnected data. It precisely mirrors the actions, configuration, and interactions of its physical equivalent within a digital domain. In healthcare, the principles of digital twins are applied with a focus on organs, tissues, patients, communities, or even entire hospital facilities. By using IoT platforms, sophisticated computational models, and advanced AI and ML algorithms, these virtual copies provide an in-depth understanding of their real-world counterparts. Digital Twins in Healthcare

Source: HiNZ To ensure a comprehensive real-time digital representation, digital twins rely on healthcare data from a variety of sources: Green and white infographic titled “Healthcare Data from a Variety of Sources” lists data types from Personal, Hospital, and Community sources, each with subcategories marked by checkmarks. This comprehensive overview aims to improve patient outcomes by leveraging healthcare data through digital twins technology.

Green and white infographic titled “Healthcare Data from a Variety of Sources” lists data types from Personal, Hospital, and Community sources, each with subcategories marked by checkmarks. This comprehensive overview aims to improve patient outcomes by leveraging healthcare data through digital twins technology.

Now, let’s dive deeper into how digital twins usher in an era of precision, prediction, and empowerment in patient care.

Unlocking the next frontier of personalized medicine

At the heart of personalized medicine lies the ability to tailor healthcare interventions to suit the individual characteristics of each patient. Digital twins are pivotal in this paradigm — drawing from extensive datasets and modeling the complex mix of environmental and genetic factors, they construct personalized models that capture the nuances of a patient’s biology.

Personalized treatment plans

With the help of digital twins, clinicians can explore different treatment paths to identify the most effective option with minimum adverse effects, predict drug responses based on the patient's genetic profile, and adjust dosages to optimize outcomes.

Simulating disease progression

Researchers can model disease progression within digital twins to gain valuable insights into complex conditions and even predict milestones. Armed with these insights, physicians can stay vigilant for early indicators of decline or potential complications in individual patients.

Anticipating health trajectories with predictive analytics

Digital twins enhanced with predictive analytics capabilities enable healthcare professionals to proactively anticipate and mitigate health issues. For instance, researchers at Mayo Clinic are exploring how digital twins can be leveraged to predict patient reactions to therapies in the vital initial 24-hour period of sepsis. Through continuous monitoring and analysis of patient data, digital twins can spot early disease markers, recognize subtle deviations from normal health, and pinpoint patients at increased risk of adverse outcomes. This predictive intelligence empowers healthcare providers to implement preventive measures, adapt treatment strategies, and provide patients with personalized health recommendations.

Improving community well-being

Digital twins can also be powerful allies in improving health outcomes at the community level by providing valuable insights into population health trends and health disparities.

Epidemiological monitoring

Digital twins empower public health authorities to track the spread of infectious diseases and recognize emerging health risks in real time. Armed with a holistic understanding of disease patterns, healthcare providers can proactively implement measures to contain outbreaks, mitigate transmission, and protect population health.

Emergency readiness

By using digital twin technology, emergency responders and paramedics can model diverse disaster scenarios. So when a disaster does strike — whether it is a pandemic, natural calamity, or industrial accident — emergency personnel are well prepared for efficient evacuation and coordinating relief efforts.

Targeted public health campaigns

Underpinned by rich population health datasets, digital twins can pinpoint high-risk groups and forecast upcoming health trends. This empowers public health officials to tailor evidence-based interventions like vaccination campaigns, screening programs, and engaging health education initiatives.

Accelerating clinical research

Around 80% of clinical studies face enrollment delays, and 20% fall short of their enrollment targets. The difficulties in this phase, such as finding suitable participants who meet precise criteria, contribute to the escalating cost and duration of clinical trials. Digital twins present a solution: they can generate an unlimited number of virtual patients that serve as the control group. This approach expedites clinical research, lowers risks, and decreases the need for costly trials to validate new therapies. Unlearn, an AI company, leads the charge in using digital twins to speed up clinical trials. Their innovative approach involves generating virtual replicas of patients to serve as controls in clinical trials across various domains — neuroscience, metabolic disease, and more. Recently, Unlearn secured $50 million in a Series C round, bringing their total funding to over $130 million in seven years. This remarkable investment underscores the critical role and immense potential of digital twins in advancing clinical research.

Optimizing hospital operations for greater efficiency

In addition to their direct impact on patient care through personalized medicine and preventive analytics, digital twins can also improve patient outcomes through better operational efficiency of hospitals. With the ability to mirror complex hospital operations, they enable administrators to model and analyze various scenarios without disrupting actual processes.

Optimized resource allocation

Digital twins offer immediate insights into resource usage trends, demand predictions, and operational bottlenecks, empowering administrators to strategically allocate resources and optimize staffing, equipment utilization, and inventory management to meet patient needs.

Workflow optimization

Through modeling patient journeys and analyzing workflows, digital twins improve care delivery, decrease wait times, and elevate the overall patient experience.

Predictive maintenance

Maximizing the lifespan of hospital assets is also much easier with digital twins. Thanks to embedded predictive analytics capabilities, digital twins can forecast equipment maintenance needs, schedule proactive repairs, and optimize maintenance schedules.

Key takeaways

What was previously confined to the realm of science fiction is now a reality — digital twins are indeed a promising technology that can revolutionize how we diagnose and treat patients. From streamlining clinical trials and drug research to delivering personalized medical interventions tailored to individual genetic profiles, and even launching targeted healthcare initiatives for specific community groups — digital twins hold great potential for enhancing patient outcomes. With over 25 years of experience in delivering custom healthcare solutions, we at Kanda Software keep our finger on the pulse of technological advancements to help our clients stay ahead of the curve and create new growth opportunities. Talk to our experts today to see how you can leverage our cutting-edge expertise in big data analytics, AI and machine learning to improve the delivery of patient care.