Healthcare simulation relies on models, manikins and digital representations to simulate elements of professional care. Referred to as “digital twin,” dynamic digital representations of a product or service (like a mechanical ventilator) allow professionals to monitor progress, diagnose issues and test solutions remotely in a simulated way. By simulating real-life conditions, these digital models help professionals to modify systems in real time with the utmost precision without potential risks to harming patients. Here we look closer at the concept of a digital twin in healthcare education and training — and how it can be applied to medical simulation.
Specifically, in terms of healthcare, a digital twin can be used to depict the well-functioning of technology, such as an X-ray machine or MRI scanner. To provide accurate real-time status of a physical device, a digital twin is continuously fed with data from embedded sensors and software. Further, artificial intelligence (AI) has even made identifying potential issues possible before they occur in real life. Having a personalized digital twin to represent a patient or patient population can be developed to test treatments and simulate different scenarios brings the possibility of personalized and proven medical intervention.
Enacting a digital twin is a transformative approach to creating a complex computing architecture to connect different parts of the healthcare system. The field of healthcare can greatly benefit from a digital twin because identifying critical issues before they happen can improve patient outcomes while increasing patient safety. Proactive remote monitoring allows healthcare leaders the opportunity to rectify impending issues remotely and to schedule maintenance by a service engineer as appropriate, impacting the fewest number of patients.
Through this process, lessons can be uncovered and learned, leading to opportunities within the virtual environment that can be applied to the physical world to transform a company’s processes. Digital twin technology additionally helps companies improve the customer experience by better understanding customer needs, develop enhancements to existing products, operations, and services, and can even help drive the innovation of new business.
Health technology company, Phillips, explains in the article “The rise of the digital twin: how healthcare can benefit” that, “It is impossible to eliminate the need for maintenance, however. For example, just like you might need to replace the fan belt in your car or the chain on your bicycle after a while, certain components of an MRI scanner degrade over time through regular use. The challenge, then, is to identify potential problems before they occur, so you can schedule maintenance at a time when the equipment is not in use (for example, at night).”
At Phillips, as digital twin solutions were developed, the company collated technical data from over 15,000 MRI, CT and interventional X-ray systems and analyzed billions of data points. Phillips was able to identify patterns that foreshadow specific impending issues after curating the data, using machine learning (AI) and other analytical methods. The company explains that a digital twin of a device consists of four components:
- Device data
- AI / Data analytics
- Device knowledge
- Physics-based device modeling
These four components of a digital twin allow Phillips to generate useful predictions about medical equipment – helping hospitals to achieve uninterrupted workflows. When built up during product development, they also enable rapid prototyping of new or improved technology. Other components that create optimal digital twin scenarios include the technology being related to physical assets that are out of reach of direct human intervention and if they are connected to constant data feedback.
Digital twins must also be adaptable so that they are flexible enough to react to changes in a physical asset. Being threaded, of a compilation of different simulations mirroring different facets of a product or service, can be helpful to account for different aspects of performance. Lastly, a digital twin should be responsive in that they can be quickly adapted and re-engineered as necessary.
Historical Use of Digital Twins
What started from mechanical twins (the Antikythera) and moved to analogue twins (Tide Prediction) has transformed into the use of digital twins over the decades. The first instance of a digital twin being enacted to help address a technological issue occurred when the astronauts aboard Apollo 13 encountered an oxygen tank explosion in 1970. From 200,000 miles away, the team at NASA was able to enact a physical model of the spacecraft to simulate the conditions being experienced. Additionally, this digital twin allowed engineers on earth to test possible solutions.
Ultimately, the NASA team was able to create an improvised air purifier and instructed the astronauts on how to build it with materials available in the spacecraft. Due to the simulations completed at the Houston and Kennedy Space Centers, the astronauts were able to survive carbon dioxide in Apollo 13’s lunar module, having risen to life-threatening levels.
This success can be attributed to the flight controllers and Apollo 13 crew, having been well-rehearsed through simulation. Before launch, NASA’s first digital twin was used to define, test, and refine “mission rules.” The mission rules were the instructions that determined the actions of mission controllers and astronauts in critical mission situations. Among the many simulators, the command module simulators and lunar module simulators occupied 80 percent of the Apollo training time of 29,967 hours.
“The simulators were some of the most complex technology of the entire space program: the only real things in the simulation training were the crew, cockpit, and the mission control consoles, everything else was make-believe created by a bunch of computers, lots of formulas, and skilled technicians,” Gene Kranz, NASA Chief Flight Director for Apollo 13, had said.
As time progresses and the use of digital twins expands, many companies are turning to a digital twin to assist in business operations and preparations. GE’s “digital wind farm” opened and is used to inform the configuration of each wind turbine prior to construction. Rolls-Royce is another example of how their use can help enhance business. The company uses digital technology to design and test engines and service and manage them remotely through their own digital twin. Amazon’s Alexa is another example of a digital twin, using everyday actions and requests to predict future behaviors. Ultimately, digital twins are the future of healthcare delivery and the improved patient experience — and medical simulation is a perfect program to experiment with and adopt this unique technological innovation.
Are you utilizing digital twin technology in your clinical simulation program? Email us and let us know how!
