What Can Digital Twins Do for Healthcare?

A digital twin is a complex technology that, when properly implemented, can effectively solve complex problems. Learn how digital twins work and their importance to the future of healthcare.

Imagine being able to predict the impact of experimental treatments on a patient without risking their life. Or generating a digital representation of a person’s body–down to the cellular level–to choose the best surgical option. What if we could determine the odds that a pacemaker will keep a patient with congestive heart failure alive without the need for surgery? Welcome to a new world of precision medicine and preventive care, made possible with “digital twins”. 

It is possible for life sciences companies to develop personalized treatments more rapidly, for physicians to make more precise clinical decisions, for patients with chronic conditions to receive customized treatment options that extend and improve their lives, and for resource-starved hospitals to maximize staffing, workflows, and capacity more effectively. 

It’s the era of digital twins. In fact, the global digital twin market is expected to grow at a compound annual growth rate of 39.1% from 2022 to 2030. 

But what are digital twins? 

It’s easy to say that digital twins are digital representations of physical objects, but that’s an oversimplification. Digital twins are highly complex models that use artificial intelligence (AI) and large amounts of digital and physical data to accurately mimic a real-world object. That object could be a process (a production line, for example), a person, device, or system. Digital twins have even been built to represent and understand regions and cities. 

Creating a digital twin begins with the mapping of a physical object or space through imagery and LiDAR, down to every minute detail. After a digital replica is created, real-world data is added to the digital twin. The two are continuously synchronized, ensuring the twin always has the most current information. Computer vision, AI, and machine learning process the information, allowing users to model possible scenarios and outcomes on the twin’s real-world counterpart. 

While digital twins have been a mainstay in manufacturing and other industries, the healthcare industry has only begun to benefit from their power. 

Though use of digital twins in healthcare is still in a relatively nascent stage, there have been some remarkable breakthroughs. Scientists are using 3D digital twins to mimic the cells in patients’ hearts to determine if surgery is warranted, too risky, or necessary, significantly decreasing the time to operation. 

Digital twins are also being used by healthcare and life sciences organizations around the world to fulfill the promise of personalized medicine. This includes allowing physicians to leverage digital care-backed clinical decision support solutions and potentially thousands of variables to intelligently model the best course of treatment at the point of care. 

And yet, even greater potential exists. Research into use of digital twins has been expanded to new areas of digital care, including: 

Medical device development. Regulators are exploring the use of digital twins for modeling personalized medical devices. This includes advanced manufacturing of personalized prosthetics, assistive technology, and other equipment. 

Disease modeling. Digital twins are being used to study diseases such as Alzheimer’s and multiple sclerosis to better understand treatment options and accelerate trial timelines. 

Digital therapeutics and virtual reality-based therapies. With clinical evidence and real-world data, digital twins can create simulations of new treatments and bring lifesaving innovations to market more quickly. As software-based treatments gain FDA clearance, such as virtual reality therapies and prescription digital therapeutics, digital twins could also help prove these products will perform well outside a clinical trial setting.

Advanced understanding of the human body. Digital twins of organs, a genome, or a single cell can be generated, allowing researchers to experiment with innovative treatment and surgical options. 

Hospital operations. Digital twins can be used to replicate staffing systems, capacity planning, workflows, and care delivery models to improve efficiency, optimize costs, and anticipate future needs. 

Equipping a healthcare organization for digital twins requires investing in a range of technologies and processes. 

Here’s a brief checklist of the essentials: 

  • Establish priorities and objectives. What is the organization trying to achieve? What are its desired outcomes? Establishing goals upfront will help the organization determine which digital twins to create.
  • Connected infrastructure. Creating digital twins requires a lot of data pulled from many different sources. Investing in a connected infrastructure consisting of sensors and IoT devices, application program interfaces (APIs), and similar technologies is necessary for data collection.
  • Modeling and analytics technologies. AI, machine learning, predictive analytics, and 3D modeling are all key to creating effective digital twins and building accurate data models.
  • A data lake. Digital twins require a common data source that can hold large amounts of structured, semistructured, and unstructured data. Thus, it’s prudent that organizations invest in the creation of data lakes that allow for rapid data ingestion and advanced analytics.
  • Data scientists. Digital twins are most often built by data scientists who are skilled at researching the physics of the objects they’re interested in emulating. Although digital twin building software has made the process much easier, it’s still a good idea to assign the creation of digital twins to experts who are familiar and have worked with large datasets and complex algorithms. 

Digital twin technology will continue to evolve in healthcare. 

There are many factors that will drive this evolution over the next several years, including: 

Advancements in trusted AI. The success of digital twins in healthcare depends on users being able to trust the information they are presented with. That means the AI behind the models must be trustworthy. This will require an emphasis on trusted AI–the ability to see and understand the reasoning behind the predictions and diagnoses generated by the digital twin.

A focus on data stewardship and security. The handling of patient data will also be a major point of discussion. Organizations will need to establish their own rules and outline practices for data stewardship and security to protect patient information and maintain the integrity of the data. 

The proliferation of digital twins as-a-service. The market for outsourced digital twin services through major technology providers will continue to expand. This will elevate the mainstream appeal of digital twins and make it more feasible for healthcare organizations of all sizes to create their own. 

The ability to create digital twins of healthcare facilities. Organizations will create enterprisewide digital twins of healthcare facilities, resulting in significant operational benefits. For example, digital twins of hospitals will be used to identify bed shortages, manage staff schedules, identify the spread of germs, and more. 

The ability to digitize the human body. While the technology exists to digitize parts of the human anatomy, the ability to create an accurate digital twin of a human body is still on the horizon. That’s because creating a digital replica of a body requires intense virtual simulation. However, there is progress being made in this area. Within the next couple of years, that progress will likely result in doctors being able to identify previously undiscovered illnesses, predict how the body will react to certain treatments, and even identify and predict brain aneurysms.

Increased data collection for better-trained models. A steady increase in the number of IoT-enabled devices and endpoints will drive the collection of the massive amounts of data necessary to support digital twins. Digital twins will be able to pull information from a vast array of sources–from scans and medical records, to tests such as ECGs–to improve treatment and outcomes. 

Continuous interface improvements. User interfaces will steadily improve, making it easier for non-technical users to navigate digital twins and discern findings and intelligence. 

Succeeding with digital twins requires the help of a partner that understands the technology and the processes needed to bring them to life. 

A digital twin is a complex technology that, when properly implemented, can effectively solve complex problems. Guidehouse has decades of experience developing innovative strategies and building, deploying, and supporting advanced technologies for life sciences companies and healthcare organizations. We understand how digital twins work, how to implement them, and their importance to the future of healthcare. 

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