Dr Visa Suomi, medical devices industry manager at MathWorks, discusses how digital twins can improve overall patient outcomes and enable safer medical devices.
MathWorks
Medical Devices Examples
Today there are numerous devices that monitor and collect data on our physiological state: wearable smart watches and fitness trackers, medical imaging devices, digital health apps, thermometers etc. Similarly, these devices themselves are producing large amounts of data about their current operational environment and condition. But how can we comprehend all these data and get meaningful insights from it?
One possibility is to create a digital twin – a virtual representation of a physical object or a system across its lifecycle. This means the digital twin entails both up-to-date and historical data about the state of its real-world counterpart. Incorporating these dynamic data into the virtual representation for different medical applications enables proactive decision making, process optimisation, and complete lifecycle management in healthcare.
Part 1: Digital twin of a human
To create a human replication, the patient must first have their vital signs monitored in combination with anatomical and physiological data. With a variety of wearables available, a patient does not have to be in hospital to collect this data as it comes from multiple sources.
Mathworks
Dr Visa Suomi
For example, a smart watch can collect real-time information about the blood pressure, body temperature, pulse rate, sleep patterns, and overall physical activity levels of the patient. Similarly, when the patient visits a clinic or a hospital, the virtual patient model can be updated with the data from the laboratory tests and diagnostic imaging studies conducted during the visit. Genetic and behavioural data as well as social determinants of an individual can also be coded to the digital twin. When all these data are combined into a single virtual representation of a patient, a more complete picture of the medical history is available to support decision making.
MathWorks
Figure 1
There are many potential applications for these virtual replicas of humans. For example, a digital twin of a patient together with AI models can be used in precision medicine to make proactive decisions about the right treatment options for the specific patient. A virtual human model could also be used for testing novel medical therapies and drugs that would otherwise be too risky or time-consuming if conducted on a real patient, as addressed by the U.S. Food & Drug Administration. For example, a selection of chemotherapy drugs could be tested against the genetics and physiological processes of the patient to identify the best treatment response. Virtual models of individual organs can also be used in developing and testing new medical devices, such as heart models for designing pacemakers. These studies are commonly known as in silico medicine, which can be used to support or potentially replace clinical trials in the future. For patients, digital twins mean better overviews and proactive management of fitness levels, chronic diseases, and overall health status. When patients have access to all data on their physical state, they can make more informed choices around personal health.
Part 2: Digital twin of a medical device
Digital twins of medical devices and medical technology can be created as a virtual representation of a device in operation. Here, the digital twin will capture the physical properties, environment, and operational algorithms, a combination of different signals from embedded sensors can be used to gather information about the current health condition, configuration, and maintenance history of the device.
For example, the chiller in an MRI scanner can provide data about the historical operation temperatures of the imaging device, which could directly affect the remaining useful life of its parts. In addition, a large variety of other types of signals, such as vibrations, pressures, fluid levels, and electrical voltages, as well as environmental parameters and device performance metrics can be collected to build an up-to-date virtual representation of a medical device.
MathWorks
Figure 2
Medical devices are often safety-critical, and their failure could put patient lives at risk. Therefore, health monitoring and maintenance of the device are crucial. Typically, the maintenance of a medical device is conducted either reactively or preventatively. In reactive maintenance the repair work is conducted only when the failure happens, which increases device downtime and might cause safety risks to a patient. In preventative maintenance the parts are changed proactively before the failure, when they still might have useful service life left. This approach is safer to the patient but increases costs due to more frequent maintenance. A digital twin combining the historical data of the device operation with machine learning models can be used to investigate the cause patterns that lead to failures before they arise. Also known as predictive maintenance, this approach maximises the service life of device parts without compromising patient safety. Thus, a digital twin allows efficient and safe life-cycle management of a medical device.
Part 3: Digital twin of a hospital
On a larger scale, digital twins can be used to simulate entire medical facilities and reproduce their dynamic operations, improve safety, and optimise daily operations. Simulations of radiology departments, intensive care units, operating rooms and patient waiting areas can show the digital schematic of their floor designs, equipment locations, and logistics. A digital medical facility could also include operational data from hospital information systems such as staff schedules, administrative tasks, and financial transactions. If all these data were combined in a virtual representation, the optimisation of their operations could be achieved in a matter of days or weeks rather than after years of trial and error as in a physical environment.
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Figure 3
Virtual replicas of medical facilities could be used to increase equipment utilisation rates and therefore their medical examination and operation capabilities. For instance, by visualising the logistics of patient arrival all the way to finishing the examination and departure, the processes causing the biggest delays could be identified and optimised for faster patient turnaround times. This would result in shorter patient waiting durations and have a positive impact on customer and staff experience. Similarly, the staff schedules could be optimised according to the demand in clinical services. If the A&E department sees the highest demand for emergency care at certain times during the week, the personnel resources could be dynamically adjusted to better account for the demand. A digital twin of a hospital would allow smarter resource allocation and increased operational flexibility without compromising clinical safety. All these aspects together contribute towards more patient-focused medical care and data-driven decision making.
Accelerating digitalisation in healthcare
The field of digital twins is continuously evolving with more sophisticated virtual models being enabled by better computational capabilities.
Hospitals, clinics, and medical device companies are also collecting increasingly more data to incorporate into these models, which makes them more accurate representations of their real-world counterparts. Digital twins are a step towards more personalised and value-based healthcare for patients. For healthcare providers and manufacturers, digital twins allow efficient process optimisation and better product life-cycle management.
