Transforming healthcare with Digital Twins

The digital revolution is reshaping industry. Healthcare is no exception, where one of the most significant advances is the emergence of “digital twins”, digital replicas of physical systems. Developed initially for engineering and manufacturing, digital twins are now being used in medicine to improve patient outcomes, support innovation, streamline operations, reduce risks and enhance training.

 

In healthcare, a digital twin mirrors a human body, or an individual organ. It can also replicate the operational workflow of a hospital or a surgical procedure. These models are constructed using data from sources such as medical images, electronic health records, wearable devices and IoT-enabled medical equipment.

 

Improving patient care

 

Over the past 100 years, patient care has been transformed by new drugs and antibiotics, new forms of surgery including organ transplants, and technologies such as MRI scanners. Digital twins are set to take this revolution forwards very significantly.

 

Digital twins enable personalised medicine and highly tailored treatment plans by simulating how a patient’s unique physiology has, or is likely to, respond to specific treatments, based on their genetic makeup, lifestyle or medical history. For instance, a cardiologist can use a digital twin of a patient’s heart to predict responses to medications or procedures, optimising outcomes and minimising risks. 

 

By using data from medical records and wearable devices, practitioners can quickly identify the root cause of a problem and test potential treatments virtually to see what is likely to work best for the patient, reducing trial-and-error. This allows them to make faster, data-driven decisions more confidently, saving time, improving outcomes and reducing stress.

 

Chronic diseases can be managed more effectively. For conditions such as diabetes or hypertension, digital twins can take the data from real-time monitoring and simulate the effects of lifestyle changes or medications. This proactive approach helps prevent complications and improves disease management, not least by offering evidence to patients about how a change in lifestyle might improve their chances of a healthy life.

 

Innovation in medical research

 

Despite the huge amounts spent globally on medical research, there are many diseases that are still incurable – while other diseases, previously treatable with common antibiotics, are developing resistance to multiple treatments.

 

Digital twins will aid the battle against disease by assisting in drug development. Pharmaceutical researchers use digital twins to predict how new compounds will interact with the human body. They can identify the most promising candidates for clinical trials, making them more efficient and cost-effective. And they can reduce the risk of trial failure by predicting adverse effects or ineffective outcomes before testing begins.

 

As well as accelerating the development of effective drugs with fewer side effects, this approach reduces reliance on animal testing as well as enabling virtual clinical trials with a more diverse, real-world population than might be available to researchers in a particular location.

 

Digital twins can also help in the design of smart medical devices where they can simulate device performance within specific anatomical environments and physical surroundings (such as situations where unusual levels of heat, vibration or dust exist), ensuring efficient functioning and safety before new devices are put into production.

 

Operational efficiency

 

In the UK, productivity in NHS hospitals has fallen since the pandemic, despite more money and more staff. There are many reasons for this, including poor management, but one of the solutions may be the wider use of digital twins for planning hospital operations.

 

Digital twins simulate patient movement through the hospital, from admission to discharge, identifying bottlenecks and delays. This helps hospitals streamline processes in critical areas such as emergency departments and operating rooms. Wait times are reduced by allocating resources where they are most needed, and smoother transitions between departments and wards are facilitated. Bed management can be greatly improved, while staffing levels can be aligned with patient volumes to reduce stress on healthcare workers while maintaining quality care. In addition, digital twins can be used to ensure critical equipment, such as ventilators and imaging machines, is available when and where it is needed.

 

Another critical area is emergency preparedness. Digital twins can model emergency scenarios, such as natural disasters or pandemics, to help hospitals prepare for and respond to crises. Patient surges can be simulated to test resource allocation and staffing strategies, and gaps in emergency plans and response protocols can be identified and optimal solutions, which will have minimal negative impact on other hospital functions, can be defined.

 

Risk reduction

 

The medical profession can, rightly, be risk averse. But digital twins mitigate many medical risks by enabling healthcare providers to test interventions before applying them in real-world scenarios. Doctors can experiment with treatments or medications on digital replicas, avoiding adverse effects on live patients.

 

In addition, digital twins allow for enhanced training for medical professionals. For instance, complex or innovative procedures can be practised in virtual environments, reducing unforeseen complications during surgery. Doctors can simulate rare conditions, helping them to recognise them when they see them in the real world. And they can stay current with advancements without relying solely on experience.

 

Challenges to adoption

 

Despite their immense potential, the implementation of digital twins in healthcare faces several hurdles which will need to be addressed. These include data privacy and security. The sharing of sensitive healthcare data to build digital twins demands robust safeguards to protect patient privacy.

 

In addition there may be related ethical considerations. No digital twin can be an exact replica of the real world and there may be biases in algorithms, or faults (such as inaccuracies or incompleteness) in the data used to power the digital twin that may result in damaging outcomes. And the implications of acting too quickly on predictions that are likely but uncertain need to be examined carefully.

 

Despite these challenges, advances in artificial intelligence, machine learning and cloud computing will undoubtedly enhance the capabilities of digital twins, as will the increasing amount of experience that the medical profession has with these systems. Over time, more accurate predictions, and even real-time updates, will become routine.

 

As these technologies evolve further, digital twins will become an integral part of healthcare, transforming medical treatments, advancing research and improving operational efficiency.