Column – Next Generation Digital Twins in Healthcare
Why should we care about digital twins in healthcare?
Look around and see countless examples of devices that track and collect information about our physiological state: wearable smart watches and fitness monitoring devices, medical imaging devices, digital health applications, thermometers, etc. Similarly, these devices also generate large amounts of data about their current operating environment and status. But how can we understand all this information and gain meaningful insights from it? One possibility is to create a dynamic representation of this information called digital twins.
A digital twin is a virtual representation of a physical object or system during its life cycle. This means that the digital twin has both up-to-date and historical information about the state of its real world. Incorporating this dynamic data into the virtual presentation of different medical applications enables proactive decision-making, process optimization, and management of the entire healthcare lifecycle.
Part 1: The human digital twin
When replicating a human or patient, a digital twin is formed using vital signs monitoring in conjunction with anatomical and physiological data. In the world of ubiquitous devices and biomedical sensors, this information can come from a variety of sources. For example, a smartwatch can collect real-time information about a patient’s blood pressure, body temperature, pulse, sleep pattern, and general physical activity. Similarly, when a patient visits a clinic or hospital, the virtual patient model can be updated with data from laboratory tests and diagnostic imaging studies performed during the visit. In addition, genetic and behavioral data as well as individual social factors could also be coded for the digital twin. When all this information is combined into a single virtual representation of the patient, a more complete picture of the patient’s medical history is available to support decision making.
There are many potential applications for these virtual replicas of humans. For example, a patient’s digital twin, along with artificial intelligence models, can be used in precision medicine to make proactive decisions about the right treatment options for a particular patient. The virtual human model could also be used in a simulation to test new medical treatments and drugs, as discussed by the FDA, which would otherwise be too risky or time consuming if performed on a real patient. For example, a range of chemotherapy drugs could be tested against a patient’s genetics and physiological processes to identify the best response to treatment. Virtual models of individual organs can also be used to develop and test new medical devices, such as heart models used in the design of pacemakers. These types of studies are commonly known as in silico medicine, which can be used to support or even possibly replace future clinical trials. From the patient’s perspective, a digital twin with vital signs monitoring enables proactive management of chronic diseases, fitness levels, and general health. When this information is combined, people can make more informed decisions about their personal health and thus achieve a healthier lifestyle.
Part 2: The digital twin of a medical device
In the context of medical devices and technology, the digital twin is a virtual representation of an operating device that describes its physical properties, environment, and operating algorithms. The combination of different signals from the embedded sensors can be used to gather information about the current health status, configuration, and maintenance history of the device. For example, an MRI scanner cooler — that is, a machine cooler — can provide information about the historical operating temperatures of an imaging device, which can directly affect the remaining life of its components. In addition, a wide variety of other signal types can be collected, such as vibration, pressure, fluid levels, and electrical voltages, as well as environmental parameters and device performance meters, to build an up-to-date virtual representation of the medical device.
Medical devices are often safety-critical and their failure can endanger the lives of patients. Therefore, monitoring and maintaining the health of the device is very important. Typically, maintenance of a medical device is performed either reactively or prophylactically. In reactive maintenance, repairs are performed only in the event of a fault, which prolongs the downtime of the device and may pose safety risks to the patient. In preventive maintenance, parts are replaced proactively before failure, when they may still have a remaining service life. This approach is safer for the patient, but it increases the cost due to more maintenance. With a digital twin that combines historical data on device operation with machine learning models, the causes of failures can be investigated before they occur. This approach, also known as proactive maintenance, maximizes the life of device components without compromising patient safety. The digital twin thus enables efficient and safe life cycle management of the medical device.
Part 3: The hospital digital twin
Digital twins can also be thought of on a larger scale, where complete hospitals or organizations can be simulated along with their dynamic functions and processes. An example of this would be the hospital’s digital twin, where departments and resources could be modeled to optimize day-to-day operations and improve safety. These conditions can be, for example, an intensive care unit, a radiology department, a patient waiting area or an operating room. Simulations of these areas may include a digital diagram of their floor plans, equipment locations, and logistics. A digital medical facility could also include operational information obtained from hospital information systems, such as staff schedules, administrative tasks, and transactions. If all this information were combined into a virtual presentation, optimization of its operation could be achieved in a few days or weeks and not after years of trial and error as in a physical environment.
Virtual replicas of medical institutions could be used to increase the utilization rate of devices and thus their medical examination and performance. For example, by visualizing the logistics of patient arrival all the way to the end of the study and departure, the processes causing the greatest delays could be identified and optimized for faster patient lead times. This would shorten patient waiting times and have a positive impact on the customer and staff experience. Similarly, staff schedules could be optimized according to the demand for clinical services. If the Accident and First Aid Department sees the most emergency assistance at certain times of the week, human resources can be dynamically adjusted to better meet demand. The hospital’s digital twin would allow for smarter resource allocation and operational flexibility without compromising clinical safety. Together, these aspects contribute to more patient-centered care and data-driven decision-making.
Accelerating digitalisation in healthcare
Digital twins are still a relatively new concept in healthcare, and the industry is constantly evolving with more sophisticated virtual models that allow for better computing capabilities. Hospitals, clinics, and medical device companies are also gathering more and more data to incorporate these models, making them more accurate representations of their real counterparts. Digital twins are a step towards more individual and value-based patient care. For healthcare providers and manufacturers, digital twins enable efficient process optimization and better product lifecycle management.