Picture this: a virtual model of a patient, a hospital, or even an entire health system – always up to date, helping you plan, predict, and make smarter decisions.
That’s the promise of digital twins in healthcare. But turning that promise into reality isn’t simple.
Fragmented data, outdated IT systems, and privacy concerns make adoption feel out of reach for many healthcare providers.
Still, digital twins are already making an impact. Hospitals are using them to improve operations, support personalized care, and guide critical decisions – not in the future, but right now.
In this article, we’ll break down what digital twins are, how they’re being used today, and what it really takes to make them work.
Let’s dive in!
Table of Contents
What is a digital twin?
A digital twin is a virtual model that mirrors a real-world object or system.
It’s not just a static copy – it evolves alongside its physical counterpart by continuously receiving real-time data.
This concept first took off at NASA, where it was used to monitor spacecraft. Today, it’s finding its place in other industries, including healthcare.
The digital twins in healthcare market is expected to rapidly grow in the coming years, growing from a value of $4.47 billion in 2025 to an impressive $59.94 billion by 2030.
In healthcare, a digital twin can represent many things: a hospital unit, an individual patient, or even a specific organ.
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These models are built using data from electronic health records, medical imaging, wearable devices, and even genomics.
The goal is to create an accurate, up-to-date reflection of real conditions.
What makes digital twins especially useful is their ability to change as new data comes in, unlike static reports or one-off simulations.
This opens up new possibilities for testing scenarios, running predictions, and supporting decisions without impacting actual patients or hospital operations.
Healthcare digital twins vary in scope:
Patient-level twins focus on supporting personalized care.
Hospital-level twins help analyze workflows and manage resources.
Population-level twins model behavior of communities/regions/countries.
While the potential is exciting, digital twins aren’t a silver bullet.
They’re a tool that adds context, giving healthcare teams data-backed insights to support smarter decisions.
Examples of digital twin solutions in healthcare
Like we said, digital twin technology is already being put to use in healthcare.
These solutions tackle practical challenges, from hospital operations to patient-specific care planning.
We’ve included both commercial products and research prototypes, along with their primary developers, main use case, and current status:
Collaborative R&D on digital twin infrastructure for health conditions like lung cancer, sepsis, diabetes, mental health
Research
Next, we’ll cover the main types of digital twins in healthcare in more detail.
Types of digital twins in healthcare
Here, we’ll go over the 3 key types of digital twins: patient-level, hospital-level, and population-level twins.
Patient-level digital twins
While hospital-level twins focus on operations, patient-level digital twins are built for individual care.
These models create a virtual version of a specific patient’s health profile, using real-time data from medical records, imaging, sensors, and genomics.
The goal is simple: help clinicians understand how a patient’s condition might evolve and how different treatments could affect them.
Unlike general models or population averages, patient-level twins reflect what’s unique about each person.
They help doctors safely explore treatment options, anticipate risks, and make more informed decisions, all without directly impacting the patient.
Patient-level digital twins offer a more tailored approach to care.
By simulating how a particular patient might respond to treatments,.they support personalized care plans.
This is especially valuable for managing chronic conditions or planning high-risk interventions.
For example, in cardiology, patient-specific heart models predict how structural changes or procedures might affect heart function.
These simulations provide insights into the potential outcomes of surgeries, medication plans, or device implants.
In oncology, tumor-specific twins can simulate how radiation therapy or chemotherapy will affect an individual’s cancer profile. This lets oncologists test treatment options and choose the most effective approach with the least side effects.
These applications are still evolving, but early results show digital twins can help bridge the gap between general clinical guidelines and individual patient needs.
Beyond treatment planning, patient-level digital twins also support continuous monitoring.
By feeding real-time data from wearables, implants, and other devices into the model, clinicians can:
More closely track a patient’s condition.
Spot early warning signs of deterioration.
Predict flare-ups intervene before things escalate.
For patients with chronic illnesses like diabetes or heart failure, this proactive approach can improve outcomes and reduce hospitalizations.
It’s important to note that digital twins don’t replace clinical judgment. They’re an additional support layer which provides added context to help clinicians make better, faster decisions.
Patient-level twins also play a role in clinical research.
Virtual models closely mimic real patient responses, so researchers can test hypotheses, explore new treatment protocols, and speed up the development of personalized therapies.
For rare diseases or complex conditions with limited patient data, digital twins can simulate scenarios that would be difficult, or even impossible, to study in clinical trials.
In the long run, these models could contribute to more effective clinical trials, faster innovation cycles, and ultimately, better care for patients.
Hospital-level digital twins
Hospital-level digital twins create dynamic, virtual models of healthcare environments.
These models show how facilities, clinical workflows, staff, and resources interact in real life. The goal is simple: to help hospitals make better operational decisions and improve patient care.
While the technology is still maturing, hospitals are already using digital twins to optimize internal processes, predict patient flow, and support surgical planning.
Running a hospital smoothly is tough. Digital twins make it easier by:
Simulating day-to-day operations.
Testing “what-if” scenarios for staffing, bed capacity, and equipment use.
Helping teams evaluate changes with data before committing time and money.
A good example is GE HealthCare’s Command Center platform, which uses a hospital-wide digital twin to support decisions on capacity management, staffing, and resource use.
Beyond day-to-day management, digital twins also help hospitals look ahead.
By constantly updating with data on patient flow and bottlenecks, digital twins help hospitals forecast admissions, plan resources, and stay ahead of demand spikes.
Siemens Healthineers and the Medical University of South Carolina developed a digital twin to simulate how optimizing facility layouts and processes affects hospital performance.
Elsewhere, hospitals like Children’s Mercy in Kansas City used GE HealthCare’s Command Center to optimize hospital-wide planning for winter surges.
For hospital administrators, these models act as a low-risk sandbox.
They give hospitals a safe way to test changes like:
Adding or removing beds
Tweaking nurse-patient ratios
Rerouting patient flows
Beyond operations, digital twins also play a growing role in surgical planning.
By creating patient-specific 3D models from imaging data, they help surgical teams prepare for complex procedures with greater precision.
For example, Philips’ HeartNavigator builds a virtual model of a patient’s heart from CT scans, allowing cardiologists to simulate Transcatheter Aortic Valve Replacement (TAVR) procedures.
Surgeons can experiment with different valve sizes and techniques in a digital environment, which reduces the risk of complications during the actual procedure.
In all these cases, digital twins are a valuable tool for rehearsing proceduresl, risk assessment, and decision-making.
Population-level digital twins
When scaled up, digital twins move from supporting individual care to informing public health strategies.
Population-level digital twins are designed to model the behavior of entire communities, regions, or even countries.
By aggregating data from healthcare systems, public health records, and environmental factors, these models help policymakers understand broader trends and plan accordingly.
While patient-level twins focus on individual care, population-level models take a step back to look at the bigger picture.
One of the key uses of population-level digital twins is in strategic public health planning. Twins can help:
Model how diseases spread through communities
Predict healthcare demand
Spot potential hotspots early
Shape intervention strategies
Population-level twins can also support planning for seasonal illnesses, like influenza, or long-term challenges such as managing aging populations.
They help answer critical “what-if” questions, providing a data-backed way to test scenarios before implementing real-world changes.
Another critical use case is resource optimization. Healthcare resources are limited. Population-level twins help ensure they’re directed where they’re needed most by:
Modeling patient flows across facilities.
Highlighting bottlenecks in the system.
Forecasting future demand.
Supporting better use of beds, staff, and equipment.
Hospitals and health systems can use this to guide infrastructure decisions, like building new facilities or expanding services.
Instead of guessing, they can simulate how different floor plans, department setups, or patient processes affect care quality and efficiency.
This reduces the risk of costly mistakes and gives planners a clearer view of how changes might play out in the long run.
Population-level twins also help support policy decisions with data.
Public health policies often involve tough trade-offs and digital twins give teams a practical way to test decisions and see how different actions could impact outcomes.
Policymakers can use population-level twins to see how changes in funding, vaccination efforts, or preventive care might affect health outcomes over time.
This helps them set realistic goals and understand the potential impact of large-scale health programs.
And while no model can perfectly predict the future, digital twins give decision-makers a better way to plan based on real data, not assumptions.
Benefits of digital twins for healthcare
Digital twins in healthcare offer real, measurable benefits.
Here’s a simple breakdown of where they make the biggest impact:
Benefits of digital twins for healthcare: summary
Benefit
What it means
Why it matters
Better decision-making
Provides up-to-date models of hospital operations, patient conditions, and population health trends. Helps teams run simulations and explore scenarios before acting.
Supports informed decisions at all levels, from bedside care to hospital management and public health. Reduces uncertainty and guesswork.
Personalized care
Creates patient-specific models using data from health records, imaging, and sensors. Simulates how treatments affect individual patients.
Helps clinicians tailor care plans to each patient’s unique needs, improving outcomes and minimizing risks.
Proactive healthcare
Continuously monitors data to spot early signs of problems in patients or in hospital systems. Allows interventions before issues escalate.
Shifts care from reactive to proactive, improving patient outcomes and reducing strain on healthcare resources.
Better operational efficiency
Models workflows and resource use to identify inefficiencies and test process improvements virtually.
Helps hospitals optimize staffing, equipment usage, and patient flow, leading to better service delivery and cost savings.
Risk-free testing environment
Provides a digital “sandbox” to safely experiment with new layouts or interventions before implementing them in real life.
Reduces the risks and costs of trial-and-error approaches and ensures changes are well-tested and data-backed.
But, getting to these benefits isn’t easy.
Next, we’ll discuss the main challenges of digital twin development.
Key challenges of digital twin development
Here, we’ll take a look at the key challenges of developing digital twins.
Data integration and interoperability
Healthcare data comes in many different formats and from a bunch of different places.
EHRs, imaging systems, wearables, lab results, genomics – just to name a few.
Information is often siloed across these different systems and getting these diverse data streams to work together in a single, cohesive digital twin is anything but simple.
The lack of standardized formats and fragmented legacy systems slows down real-time data exchange, for example.
To solve this, healthcare organizations are adopting interoperability standards like:
HL7 and FHIR for EHRs
DICOM for medical imaging
Bluetooth Health Device Profile for wearables
Clinical data models like OMOP also help bring different data sources into a unified structure.
In practice, integrating systems like a hospital’s ADT (admit/discharge/transfer) system, lab databases, and device feeds with a twin requires custom middleware or APIs.
Getting everything to integrate and work seamlessly can be a herculean task. But, without solid data interoperability, the promise of digital twins falls flat.
Luckily, the trend is moving toward standardized, plug-and-play integrations that use these interoperability standards.
Real-time data feeds
A key trait of digital twins is that they stay in sync with their real-world counterparts in near real-time.
From patient vitals to hospital admissions, they rely on a steady flow of up-to-date data.
Legacy healthcare IT systems can struggle with this.
These systems weren’t built for continuous, high-frequency data streams, which makes real-time updates unreliable.
To close this gap, healthcare providers are turning to modern solutions like IoT devices, edge computing, and cloud platforms that can handle large volumes of live data.
Edge computing, for example, processes data closer to where it’s generated, which reduces latency and keeps the twin up-to-date even when connectivity isn’t perfect.
Cloud platforms play a key role too, since they can easily handle large volumes of data from multiple endpoints.
Without these upgrades, the full potential of digital twins stays out of reach.
The ultimate goal is simple: minimize lag between the physical system and its digital twin to ensure decisions are based on current, accurate information.
Integrating AI, machine learning, and analytics
What sets a digital twin apart from a simple dashboard is its ability to reason and predict.
It doesn’t just show data – it simulates, learns, and predicts.
Machine learning models fine-tune the twin to reflect individual patient data and forecast outcomes.
Take acute disease management, for example. A patient twin might use a neural network to analyze time-series data and predict when deterioration is likely.
And organ-level twins, like Philips’ HeartNavigator, blend physics-based models with machine learning to capture patient-specific details more accurately.
But building this intelligence is only part of the challenge. These advanced models need to reliably and continuously connect with clinical systems and update as new data comes in.
That requires high-quality data, serious computing power, and tight integration with the twin’s architecture.
Thanks to the rise of open-source AI tools and growing healthcare AI expertise, this is getting easier.
Still, one thing hasn’t changed: clinicians need to understand why the twin makes a certain prediction. Black-box answers aren’t good enough.
And building that trust? That’s the hardest part.
Privacy and security
Digital twins rely on sensitive and protected health data, which brings serious privacy and security challenges.
Any system that pulls data from EHRs, wearables, or real-time patient feeds must meet strict standards like HIPAA, GDPR, and local data regulations.
Protecting this data isn’t optional. Best practices include:
Encryption for data both at rest and in transit
Strict access controls to limit who can view or use the data
Clear patient consent for how their information is used and updated
Beyond basic compliance, there’s a bigger conversation about data ownership. Patients deserve transparency and control over how their personal data gets fed into a digital twin.
And since these systems depend on continuous data streams, they’re also potential targets for cyberattacks.
To keep data secure and trustworthy, you need to implement solutions like:
Other privacy-preserving methods include:
Real-time anomaly detection systems to monitor for suspicious activity and maintain data integrity.
Federated learning, which keeps data at its source while still allowing for model training.
De-identification techniques to protect patient identity while still enabling useful insights.
Security and privacy aren’t nice-to-haves here – they’re essential for trust, adoption, and responsible use.
Infrastructure and scalability constraints
Running digital twins, especially at the patient or hospital level, takes serious IT muscle.
Handling large data volumes and running complex simulations is no easy feat.
To manage this, you need:
Cloud computing power
High-performance servers
Fast and reliable networks
And that’s just the beginning.
You also need a scalable architecture based on microservices, where different parts of the twin operate as separate services but communicate via APIs.
Data standards like FHIR ensure these components can share data smoothly.
Cloud-based digital twin platforms are helping lower the barrier of entry – these solutions can plug into your existing hospital systems and offer scalability without massive upfront investments.
But for critical, real-time applications (think ICU monitoring), edge computing is often the better choice since it processes data closer to where it’s generated to avoid delays.
As these standards take hold and infrastructure improves, bringing digital twins into day-to-day healthcare will get much easier.
But, other challenges remain.
Not every hospital has the resources to support this level of IT infrastructure. For smaller clinics, even with cloud services, the costs can be a dealbreaker.
Another hurdle: scalability. Most digital twin solutions are built for specific needs, like a specific department, procedure, or patient group. Expanding them beyond that scope is difficult.
Each new implementation needs customization, which increases costs, slows down adoption, and limits broader digital twin deployment.
And until modular, plug-and-play approaches become the norm, scalability will stay a key limitation.
Digital twins in healthcare: FAQs
A digital twin is a dynamic, data-driven model that mirrors a real-world object or system.
In healthcare, that could be a patient, an organ, a hospital unit, or even an entire region or country.
It’s continuously updated using real-time data and can be used to simulate, monitor, and predict outcomes, risk-free.
Yes, though it’s still early in terms of widespread adoption.
Some hospitals are already using them to manage operations, plan capacity, and support surgical decisions.
But, these are still mostly pilot projects or specialized implementations, although the trend is growing.
That depends on the use case.
It could include EHR data, lab results, medical imaging, wearable device data, or even genomics.
For hospital-level twins, it can also include data from infrastructure systems and equipment.
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Mario makes every project run smoothly. A firm believer that people are DECODE’s most vital resource, he naturally grew into his former role as People Operations Manager. Now, his encyclopaedic knowledge of every DECODEr’s role, and his expertise in all things tech, enables him to guide DECODE's technical vision as CTO to make sure we're always ahead of the curve.
Part engineer, and seemingly part therapist, Mario is always calm under pressure, which helps to maintain the office’s stress-free vibe. In fact, sitting and thinking is his main hobby. What’s more Zen than that?
