Digital Twin in Healthcare: What It Is, What It Does

Consider a digital representation of a hospital that precisely replicates the physical structure. Imagine now if this digital twin has the ability to mimic situations, forecast patient outcomes, and instantly improve treatment plans. 

This is how the digital twin in healthcare can prove to be powerful.

The application of digital twin technology in healthcare is changing our understanding of and approach to treating patients. Examples include digital twin hospitals that mimic operational procedures and patient-specific models that forecast the course of disease. 

A recent study states that reaching 21.1 billion dollars by 2028, the global digital twins in healthcare market is expected to develop at a compound annual growth rate (CAGR) of 67.0% from 2023 to 2028, from a projected 1.6 billion dollars in 2023.

The global digital twins in healthcare market is anticipated to develop rapidly due to advances in artificial intelligence and data analytics.

So, let us get to know more about digital twins in healthcare and what it does. 

What is a digital twin in healthcare?

A digital twin is essentially a digital replica of a system, process, or entity. As said earlier, it is a widely applied concept in many other industries and is now making its way into healthcare. 

Digital twins were initially used in product lifecycle management and manufacturing. For example, GE Aviation created digital twins to anticipate failures and do predictive maintenance. At the same time, in the architecture industry, the tech has been used to develop and analyze building designs before construction. 

Though slow, digital twins are gradually gaining momentum in healthcare as well. 

The basic concept here is to create a virtual replica of an individual's health profile. Healthcare professionals can use this digital double to simulate and predict various outcomes. We will learn about the applications of digital twins in healthcare further down the lane. 

For example, virtual organ models made using digital twins allow surgeons to practice surgery before the real thing, which improves patient outcomes. 

Now, coming back to digital twins, there are 3 main technologies it leverages. They are Artificial Intelligence and Machine Learning, Data Analytics, and Internet of Things. See how they contribute: 

1. AI and ML: This mainly analyzes the data it receives to identify patterns, predict the future, and learn from each interaction. Both AI and ML play a key role in the ongoing adaptability and usefulness of Digital Twins.

2. Data Analytics: Digital twins need to collect and analyze a vast amount of data, and that's where this comes in. It turns raw data into structured information that can be easily analyzed and interpreted. 

3. Internet of Things (IoT): IoT devices like wearables can be used to gather health-related data from individuals. The data includes heart rate, sleep patterns, and physical activity. 

So, keeping all these in mind, a digital twin of a patient with heart disease will have all of the patient's health data. Doctors can simulate different treatments on this digital model and identify more efficient ones. This ability to predict and personalize treatments can lead to better patient outcomes and efficient use of healthcare resources. 

Also read: Implementing Digital twins in Healthcare: Challenges & Solutions

Significant progress has been made in the field of medicine with digital twin technology in healthcare employed increasingly to improve processes, model patient outcomes, and provide more individualized care. 

It has been identified that approximately 66% of healthcare professionals decided to improve their investments in healthcare robots within the timeframe of three years. This trust in digital twins highlights the wonderful benefits these technological advancements bring to healthcare and operations management. 

Further, digital twins are providing healthcare facilities with a platform to explore fresh concepts and innovation, which is helpful in imagining potential futures and experimenting with infinite “what-if” circumstances. 

Research on the application of Digital twins in field medicine will grow as Digital Twins becomes more explored and big data, AI, and IoT technologies progress. The Health Market research projects that national health expenditures will reach 1795 per capita, growing at a rate of 1.50% from 2012, and that worldwide spending on IoT in the healthcare sector would reach USD 188.2 billion by 2025, growing at a rate of 21.0%.

Furthermore, by utilizing big data processing and Digital twins, simulations of high-resolution patient models may be run to identify suitable drugs or treatment strategies as well as precise therapy goals, enabling patients to get precision medicine. 

Lastly, the digital twin applications in hospitals or hospital departments enables effective scheduling of demand-oriented medical activities and management of medical resources.

Challenges of Digital Twin in Healthcare 

The key challenges of digital twin in healthcare are:

• Data security and privacy concerns

• Data precision

• Interoperability issues

• Scalability issues

• High initial costs

• Cultural resistance 

These challenges will be explained in detail in the coming section. 

Please read: Digital Twins in Medicine: The Future of Healthcare

Currently, the usage of Digital twins in healthcare is minimal, but studies indicate that it can benefit the field. 

Like how engineers can visualize entire buildings and complex structures, digital twins can create a digital model of the human body. All it requires is adequate data. 

With that said, here are some benefits of digital twins in healthcare. 

With that said, here are some benefits of using digital twins in healthcare. 

Enhanced patient care

Doctors can test treatments on a patient's digital twin before trying them on the patient. This way, doctors can understand how a patient will respond to the treatment, leading to safer and better care. 

Imagine Dr. Gregory House diagnosing his patient with a digital twin. Cool, right? Certainly, he would have comments on it. But the diagnostic would be faster and more efficient without risking his patients.

Proper training

Digital Twins can significantly improve the quality of training and education in healthcare. It helps medical students, nurses, and even professionals understand the human body and diseases in detail. 

Surgery procedure lessons and virtual anatomy are two to point out. This way, students can get hands-on experience without risking the patient's health. 

Healthcare system optimization

Digital twins can help identify bottlenecks or inefficiencies in the healthcare system. This way, hospitals can manage their resources better and improve patient flow. 

For example, there might be a long wait for a specific test or procedure. By identifying this, managers can take measures to tackle the inefficiency. In this case, they can either add more staff or schedule testing. 

Predictive maintenance

Hospitals use various types of equipment to treat their patients. This includes X-rays, CTs, MRIs, and more. And no hospital wants this equipment to fail with so many patients waiting. Or worse, fail during the process. 

But with digital twins, they can keep track of the performance and predict when a device might fail. This can prevent any unexpected breakdowns and ensure it's always ready when required. 

Case Study: Predictive Maintenance System to Prevent Malfunctioning Machines 

Research and development

Medical research is another area where digital twins can be used. Researchers can create and try out new drugs, research genetically transmitted diseases, and more. 

With proper victual replicas of the human body, they can do all kinds of research and trials. This could lead to innovations in healthcare that could be revolutionary in treating rare diseases. 

Now that we have understood how Digital twins can benefit the healthcare industry let’s look at some of their applications. 

Please read: The emerging Digital twin market in 2024

The application of digital twins is myriad in healthcare, and the numbers are only climbing. Following are some of the key applications you should know about. 

Patient remote monitoring

Digital Twins can help healthcare professionals efficiently monitor their patient's health remotely. This is particularly beneficial for telehealth settings. 

Imaging patients living remotely or those who have difficulty accessing the facilities (elderly), it's hard to keep tabs on them. But with remote monitoring, doctors can get real-time updates on their patient's status. 

Also, if the data shows any discrepancies, they could contact the patients, schedule checkups and tackle the problems before they arise. 

Clinical trials and drug development

There are a lot of hurdles when it comes to clinical trials. Especially with ethics. On whom can I test the new drug? Animal lovers are against testing on rats and other animals. And almost all are against testing new drugs on humans. 

Enter Digital Twin.

By building a proper digital model of the human body, researchers can test their developed drugs without any questions. As a result, the trials will be done faster and more efficiently without risking any lives. 

Personalized medicine

This can severely impact the "one-size-fits-all" type of treatment we are getting now. By adding all the necessary details like, eating habits, exercises, previous illness, and more, a virtual replica of the patient can be made. In doing so, they can identify the treatment best fits the patient. Not to mention rule out if the patient is allergic to any medications. 

To give you a better understanding of the application, consider the following example. With a digital twin, doctors can simulate the impact of various chemo regimens for a cancer patient. The results will help them choose the one that is more likely to be effective for the patient. Therefore, it improves the patient's prognosis. 

Surgery planning

If you are a surgeon or healthcare professional, the probability is that you have experienced or heard of surprises popping up during surgeries. Sometimes certain vessels will not be where they are supposed to be. Digital twins can help here. 

A digital twin of the patient's affected part or organ can be created with data from various sources. Using the data, digital twins can provide a 3D model for the surgeon to visualize the procedure in detail. 

For example, in complex surgery, the surgeon can identify potential issues such as proximity to blood vessels. By understanding such factors in advance, the surgeon can accurately plan the best surgical procedure. 

This way, it helps avoid mistakes and improves surgical outcomes. 

Suggested read: Digital Twins in Clinical Trials: Benefits & Use Cases

Disease modeling and epidemic management

Our world is still recovering from the recent epidemic. But what if I told you Digital twins could have helped control the spread of Covid-19? 

Yes, it could have.

A city's digital twin contains a wealth of information, such as the number of healthcare facilities, social hubs, and population density.

This data can be used by medical practitioners to simulate the possible spread of infectious diseases such as COVID-19. They can also study how different elements affect the disease's spread by varying different parameters.

Planning intervention measures and managing epidemics can be done with the information acquired.  

Prosthetics and implants

The best-fitting implants and prosthetics can be designed with the aid of digital twins. A prosthetic leg that precisely matches the size and form of the patient's remaining limb, for instance, can be made using a digital twin of the patient's leg. The patient will be more comfortable and it will suit them better this way.

Digital twins can also support a patient's recovery. Physiotherapists are able to customize the programme to meet the demands of each patient. Digital twins can also be utilized to model various motions in order to predict the patient's post-procedural abilities.

These apps create ethical and data privacy issues even though they have a lot of potential.  Therefore, while implementing digital twins in healthcare, ensure that the patient data is secured. 

Also, healthcare institutions should ensure that the use of digital twins does not result in any harm to the patient. This brings us to the important part, the challenges of implementing Digital twins in healthcare.

Suggested read: Top 7 IoT Service Providers to Maximise ROI in Healthcare Industry in 2023

Implementing Digital twins in healthcare comes with several challenges. Here are a few pointers for you to consider.

Data security and privacy

It requires a massive amount of personal health data for the use of digital twins in healthcare. The data isn't just sensitive but also the most private information individuals have. It is crucial to protect these data's security and privacy as a result.

An instance of a data privacy breach may be insurance fraud or identity theft. In extreme circumstances, a patient might even become the subject of discrimination because of their health. For example, in cases where the patient's prognosis is poor, the insurance company may decide not to cover the new medication.

For these reasons, before deploying digital twins, healthcare organizations should make sure that improved data privacy and security procedures are in place. The first step is to encrypt all data, both in transit and at rest. Ensure that the data is only accessible to those who are allowed. In order to monitor who is accessing the data and when, keep up with the audit logs.  

Best practice? Comply with regulations like Health Insurance Portability and Accountability Act (HIPAA) in the United States and the General Data Protection Regulation (GDPR) in Europe. 

These regulations put stringent requirements for the handling of personal data. 

Data accuracy and completeness

To effectively portray a patient's health, digital twins need access to a complete set of medical data. For the digital twins to be effective, the data's completeness and correctness are therefore essential.

The following are some of the elements that affect how accurate and complete data is.

1. Medical history - It's imperative to get a thorough medical history from the patient.

2. Genetic information - Genetics can be a key reason for certain diseases. Therefore it's crucial to get it, but in reality, it may not be feasible or ethical to do so. 

3. Lifestyle factors - This includes data on the patient's diet, exercises, stress levels, and substance use. 

4. Real-time health data - It's now possible to get real-time health data with wearable technologies. But the data from it can't be 100% accurate as the patient could not have always worn the device. 

As a result, collecting and integrating such a wide array of data is a significant challenge.

Interoperability

Another major challenge when it comes to implementing digital twins is interoperability. Healthcare is scattered across various systems, each having different data standards, formats, or terminologies. This makes it difficult to share and combine data accurately. 

To give you an example, the patient's electronic health record might be maintained in one system, whereas the pharmacy might use a different system to manage the medication data. The labs where they test the blood might use another system. It can be challenging to get accurate data if these systems are unable to communicate and share these data. 

One way to overcome this challenge is to implement proper data standardization and harmonization processes. 

Technical complexity

A digital twin is a cutting-edge technology that is part of the fourth industrial revolution. The level of tech expertise that is needed to create, implement and manage it is relatively high. Also, digital twins require constant updating and refinement based on real-world data. 

Other factors that add up to the complexity include - 

1. It requires sophisticated modeling and simulation techniques to create it.

2. It requires advanced data analytics to analyze and interpret the huge amount of data it produces.

3. It must be capable of integrating with existing healthcare IT systems. 

As you can see from the mentioned points, it requires a healthcare provider to be technically advanced to implement digital twins. This means only larger hospitals or technologically advanced healthcare providers can install them. 

Cost

The financial investment required for implementing a digital twin in healthcare is significant. Several factors influence the implementation cost, and the following are some of them. 

1. The cost of the technology itself is high. You need several hardware, software, and networking infrastructure, all of which are costly. 

2. The data required for digital twins must be accurate. And it is costly to collect, clean, and integrate these data into it.

3. If you implement it, you need personnel to manage it. This means you need to hire and train new blood, and there is only a handful who knows the tech. 

4. Digital Twin requires constant maintenance and updates to remain effective and secure. And this could add up the cost.

5. As discussed earlier, it requires top-notch security measures. But implementing and maintaining these measures can be costly.

6. Last but not least is consultation with tech experts. You may need to hire or consult digital twin experts to guide you with the implementation process. Again, adding to the already high cost.

Though the cost is expensive, there are ways you can reduce or lower it. One way is for you to partner with a digital twin expert and outsource your tech-related work. Although it doesn't cut the cost by half, it does lower the overall cost. 

Patient consent and engagement

Obtaining consent from the patient can be a challenging task, mostly because patients won't have a clue what they are consenting for. The concept of Digital twins is so complex that the patient may not always understand what it means or how it helps. 

Now, even if they understand what it does, they might point out the privacy concerns they have. And not to mention the long-term implication of digital twins. The patient data needs to be collected regularly and updated into the digital twin to showcase the patient's current health status. 

What can you do? Educate them.

Make the patients understand the benefits of digital twin. Be clear about the ability of digital twins to provide better and more targeted treatments for them. 

And if nothing works, try to use real-world examples where digital twins have been used to treat patients. 

Scaling

Expanding the implementation of digital twins from a small environment to a broader and more diverse set of conditions can be challenging. The set conditions can be anything from patient demography to different types of health conditions. 

For example, initially, implementing digital twins on a small scale can be feasible, like for 100 patients. But when considering a large scale like 10,000, the complexity of data that needs to be managed is significant. 

Some other challenges that you can face while scaling the digital twin are as follows. 

1. Scaling would mean more investment and therefore become more costly. 

2. You'll need proper IT and tech support to implement and manage the large-scale digital twins.

3. The need to comply with myriad regulations arises, and it is challenging to do so.

4. To effectively implement digital twins across different healthcare facilities, you will need to standardize certain aspects.

To overcome these challenges, building a robust collaboration between healthcare professionals, tech providers, regulators, and patients is essential. 

As the tech evolves, it is possible to permanently overcome some of these challenges. And the cost will also take a hit when the competition between the service providers peak.

Also read: What is the future of digital twins?

As you can see, the benefits the digital twins bring to healthcare are many. In a few years, we might even be able to tackle diseases even before they ever rise. Or, maybe faster and more efficient research could allow innovation of drugs that permanently cure incurable diseases. 

The possibility of innovation with Digital twins in healthcare is many. And healthcare institutions should try small-scale implementations of Digital twins to test whether it improves their operations. 

Yes, the tech expertise required is high. But you can always outsource them.

Leading the way are a number of digital twin healthcare companies that provide everything from personalized treatment for patients to hospital operation management services. 

Find teams like Toobler, who offer the best digital twin solutions, and connect with them to communicate your requirements. They will have a proper team and time management process set in place to ensure the timely delivery of the project. And their expertise in the domain can help further improve the quality of your digital twin. 

Pave the way to digital transformation with advanced technologies like digital twins, IoT, and AI and transform your healthcare offerings. Get in touch with our experts, and together, let's take a step toward a more personalized, predictive, and efficient healthcare future.