The notion of digital twins has spread throughout industry after
industry, changing how organizations use data to make decisions. In
healthcare, digital twins have the potential to revolutionize clinical
practices by representing patients’ biologies in a virtual realm.
These virtual models enable clinicians to understand and simulate
health states more accurately.
Fundamentally, a digital twin is a dynamic digital representation of
an individual item in the physical world – in this case, the patient.
Combining data from various sources, such as medical records and
meta-data from wearable devices, clinical notes, and real-time remote
monitoring, creates a digital representation of a patient’s state at
any given time. Digital twins are particularly applicable to
healthcare due to the need for personalized treatment and early
prediction. By leveraging digital twins, healthcare professionals can
gain new insights into patient behaviors, the evolution of diseases,
and their responses to treatment.
This article explains why digital twin technology is set to
revolutionize how organizations develop custom software for the global
healthcare industry. These digital models will improve personalized
treatment plans, enhance predictive analytics, and optimize training
simulations, leading to better outcomes for patients and lower costs
for hospitals. With digital twins, the future looks bright for
healthcare companies that want to remain at the cutting edge of
innovation and patient care.
Digital twins are high-fidelity digital simulations of real things
that use real-time data to simulate, process, and make predictions.
For health, a digital twin is a patient simulation, enabling health
providers to keep track of their condition in real-time. Through the
ongoing integration of information from various platforms, including
EHRs, wearables and other health monitoring devices, digital twins
capture a patient’s entire state, which helps clinicians make more
accurate clinical decisions and prescribe treatments that match
individual needs.
For healthcare, there are several kinds of digital twins whose uses
differ. Patient models replicate each patient so treatment plans can
be designed to fit their distinct health conditions. Organ models
model a certain organ or system of the body, attempting to mimic its
working under different circumstances—especially useful for surgical
planning or diagnosing disease. Population health models also
aggregate data across cohorts of patients so that healthcare providers
can identify patterns, manage resources, and use them for public
health actions.
Digital twin technology is built on IoT (Internet of Things), AI
(Artificial Intelligence), and data analytics. IoT devices acquire
tons of real-time patient data via wearables and medical equipment,
and AI algorithms interpret that data to provide insights and
predictions. Analytics helps translate that data into meaningful
knowledge. Together, these technologies create a compelling model for
creating digital twins and using them to help improve patients’ care
and healthcare operations.
The most potent application is as a foundation for customized
treatment plans calibrated explicitly to an individual and their
health status. Instead of testing potential treatments in the real
world, which would entail repeated invasive intervention on or in the
patient’s body, doctors could experiment with different interventions
on the virtual twin and see the result. This has obvious potential for
reducing trial and error for ‘n of 1’ treatments, dramatically
improving outcomes. As an immediate use case, consider how digital
twins could help cancer doctors predict how a patient will respond to
various chemotherapy regimens, allowing them to personalize treatments
in a way that could maximize therapeutic effects.
Another advantage of digital twins in healthcare is the potential for
predictive analytics. Digital patient models can help physicians
forecast future health outcomes based on current and prior data. For
chronic disease management, for example, a digital twin of the patient
can serve as an early warning system for the progression of the
disease or complications. Predictions based on predictive models are
expected to enable healthcare providers to intervene and avert
disaster. It could help prevent costly hospital stays.
Moreover, the digital twin can simulate a medical procedure as part of
training or preparation. It can thus represent a medical student or
other professional undergoing a complex technique as a surgeon. In
this scenario, the surgeon or technician can practice the procedure
with a virtual counterpart of the patient before performing it in real
life, helping to trial and refine techniques and anticipate the
outcome. Doing so could boost confidence and allow for better planning
and preparation, thus providing a more precise intervention and
leading to better patient outcomes. Students likewise use this sort of
simulation with the digital twin. Thus, Medical students and
professionals could use the digital twin to trial real-world scenarios
and practice techniques, improving their skills in a safe, low-risk
environment.
Digital twins begin with a source of data that can come from anywhere,
including electronic health records (EHRs), wearable devices, imaging
tools, and more. This data gets pulled from diverse sources and
organized into a single system. This massive influx of data allows
custom software developers to build a much richer model of a patient’s
health, and a healthcare software developer is empowered to make
better decisions with all of the available, up-to-date information.
Digital twins increase the intelligence of the end system. Digitally
twinned healthcare software can create a powerful, real-time,
self-updating model. Instead of looking at past occurrences and
independent dependencies that affect patients at any given time,
custom software developers can build software that can use dynamic
data to monitor patient vitals and act accordingly.
Another benefit is the precision in patient simulations. Digital twins
create virtual triplicates of patients in a minute and detailed
representation, which is useful when healthcare providers predict what
type of treatment responds ideally to an individual. Healthcare
software with better technological support, like digital twins, will
model different responses to treatment with greater reliability, which
will optimize the quality of patient outcomes. The precision in
modeling augments the human ability to prognosticate with higher
certainty and is thus better for making treatment decisions.
At last, digital twins enable a constant cycle of learning in health
software by creating feedback that continually improves software
algorithms or treatment strategies in the real world. As information
is fed back into the system, the digital twin strengthens itself and,
with more real-world results, increases its predictive power. Evolving
with every interaction, digital twins allow custom health software or
devices to learn and continuously improve the ability to anticipate
and address changing medical knowledge or patient data. With ongoing
feedback loops, healthcare providers will always have the latest tools
and software for better patient care, and software developers will,
too, continuously be improving their software via machine learning and
enhanced algorithmic capabilities for greater precision and
effectiveness.
Privacy and security is one of the primary concerns when implementing
digital twins for healthcare. For precise digital replicas, digital
twins must draw upon a tremendous amount of patient data. These
include medical records, wearable health and genetic data, which are
all very private. It is imperative to keep this information safe from
breaches, unauthorized access, and misuse. Physicians and developers
will need to ensure that there is strong HIPAA and GDPR compliance, as
well as strong encryption and security measures in place to protect
patient data on digital twin platforms.
Interoperability is another big challenge. For digital twins to be
effective, they need to seamlessly bridge a range of legacy health
technologies such as electronic health records (EHRs), diagnostic
instruments, and wearables. But many healthcare providers still rely
on legacy infrastructure that might not easily interface with recent
digital twin systems. Overcoming this barrier will take significant
technical work and partnership between developers of software and
clinicians to develop standard protocols, APIs, and architectures that
enable digital twins to talk to different systems and flow data
efficiently.
Lastly, the financial and resource requirements of using digital twins
in healthcare can be prohibitive for many institutions. It is not just
expensive to build, implement and support digital twin technologies,
it requires technical knowledge about the related fields such as data
analytics, AI, and IoT. Smaller healthcare organizations might find it
challenging to finance upfront costs, and even bigger systems should
be thoughtful about the expenses they are likely to face over time.
Also, digital twins entail advanced infrastructure like fast networks
and cloud storage that could be costly and logistically expensive to
deploy in mass.
The prospects of digital twins for healthcare remain inextricably tied
to trends and technologies that will enhance the power and reach of
the twins. For instance, advances in artificial intelligence (AI) and
machine learning will allow digital twins to process data faster and
more accurately; this will, in turn, lead to more efficient real-time
decision-making in the face of huge amounts of patient-specific
information. Similarly, the scaling up 5G networks is expected to
provide a high-speed, low-latency network platform for future health
utilities, such as remote/virtually assisted surgeries or continuous
monitoring of patients with chronic diseases. As the technological
sophistication of IoT devices and wearables increases to access
intricate health status data from the human body, the twin’s ability
to track the health of its host will become more sophisticated.
Applications on the horizon include chronic disease management and
preventive care. Virtual patients could trace the progression of
chronic diseases such as diabetes or heart disease in real patients
and help clinicians proactively manage care and prevent complications.
In preventive care, digital twin technology could serve an important
role in identifying early warning signs of disease, making it possible
to intervene before conditions become more serious or
life-threatening. Digital twins could become a must-have tool in acute
care, ongoing patient management, and health maintenance.
As digital twins start to take off, custom software development needs
to evolve to support and scale them in the most helpful and optimized
way. Product engineering adapted to the individual needs of the
healthcare providers would be required to allow digital twins to fit
naturally into the existing systems and to enable the seamless flow of
data across the boundaries of healthcare IT landscapes. Custom
solutions also offer the flexibility necessary to adjust a digital
twin model to the needs of individual medical specialties, from
cardiology to oncology. With software tailored to the development of
digital twins and designed to grow with this technology, the companies
that develop the software will be ready to support and empower
healthcare organizations in adopting digital twins, with patient
health as the ultimate target.
To summarize, digital twins can help revolutionize healthcare by offering virtual patient models; these enhance patient-specific treatments, predictive analytics, medical simulations, and more, which lead to the evolution and advancement of custom software development, data integration, patient outcomes, and continual learning. Though data privacy, interoperability, and cost are obstacles to overcome, the future of digital twins, including those to benefit healthcare, is evolving rapidly, with endless possibilities, especially in chronic disease, prevention, and more. As this shift in how we think about healthcare evolves, with a target on data and patients, healthcare software developers will be an integral part of this shift toward better, more efficient, and personalized health.