Adapting Digital Twin in Healthcare in 2024

The Proactive P4 Healthcare Model for Advanced Medical Services

Healthcare professionals are using digital twin technology to advance the quality of life through preemptive patient care. The modern implementation of digital twins in healthcare draws on AI ML solutions already integrated into modern healthcare services.

A digital twin is a virtual replica of a real object, function, or even a place. It is then used to run simulations for situational analysis. By integrating AI ML functionalities and cloud applications, digital twins can utilize real-time data and make accurate predictions regarding individual health outcomes. Digital twin technology enables health tech professionals to provide recommendations on avoiding certain illnesses and so people can stay prepared for medical emergencies.

This is made possible through the P4 proactive healthcare model, which was developed to standardize the use of digital twins in healthcare. The digital twin will act out the 4 Ps for patients: predictive, preventive, participatory, and personalized. 

Predictive

Digital twins are capable of accurately simulating the object or person that they are replicating. Healthcare practitioners provide relevant data to create a realistic digital twin.

By running simulations, patients can foresee medical emergencies through early detection of disease symptoms. They can also see the possibility of disease reversion, such as cancer.

The predictive features of the digital twin can also identify lifestyle patterns and alert people to abnormal behavior that could be detrimental to their health. 

Let’s take Joe, a hypothetical patient who has suffered from cancer in the past and is being treated with the help of a digital twin.

If Joe starts feeling low in energy, the digital twin predicts that this is due to an overproduction of insulin. Also, perhaps his cancer may be spreading to the pancreas.

Before the cancer presents critically, doctors can preemptively treat Joe to fight cancer. The digital twin utilizes Joe’s usual lifestyle and abnormal behavior to identify a change and alert doctors to the reversion of his cancer. 

Preventive

Since the digital twin can simulate and predict outcomes with precision, individuals can shift from treating diseases to maintaining healthy lifestyles. The digital twin incorporates vital data for patients, including early health markers. It uses information about early health markers, assesses the risks, and reports harmful behavior that patients should avoid to lead a healthy life.

Healthcare professionals employ Machine Learning solutions to test the efficacy of preventive plans for particular patients. They advise them on the best actions to maintain their well-being. 

The digital twin advises Joe to lower the number of cigarettes he smokes to reduce the risk of cancer. The digital twin shows Joe the risks of various scenarios where he smokes 5, 10, or 20 cigarettes in a day.

The twin empirically proves how Joe can cut down on cigarettes. It is also recommended that he add some exercise and live a long and healthy life. 

Participatory

The digital twin, through the P4 healthcare model, is interactive and easier for patients to understand. It involves patients and encourages them to make better lifestyle decisions.

The process of computing medical data through a digital twin is transparent, building trust over the course of treatment. This results in an overall improved healthcare experience benefitting doctors and patients alike. 

When a doctor prescribes lifestyle changes to Joe, he is able to comprehend their impacts more evidently. Joe sees how his actions are affecting his health and is empowered to make better decisions for himself. 

Personalized

Digital Twins become powerful healthcare tools when integrated with cloud and AI ML capabilities. To create a personalized model for each patient, the digital twin assimilates data collected from various streams, including environmental sensors, wearable gadgets, and medical devices. This enables healthcare professionals to meticulously design the digital twin and generate accurate predictions and prescriptions for individuals.

Cloud technology allows for fetching data from patients in real-time, and AI ML components run algorithms using this high-volume data for updated and precise predictions.

Furthermore, the AI programs create health indexes for each patient, thereby securing personal data and ensuring the privacy of medical records. 

Joe volunteers his geographic, sleep, and behavioral data through his smart devices, which the AI programs process along with his medical records. Then, the personalized digital twin interacts and informs Joe in real time; what lifestyle habits he should adopt and maintain to keep himself healthy.

The digital twin stores Joe’s data in the form of a health index. And a score out of 100 shows him how healthy his day has been. It, therefore, reinforces the need to make positive lifestyle changes. 

Collectively, the P4 healthcare model for the digital twin is progressing healthcare services to a truly patient-centric focus.

Dr. Ramesh Jain (Ramesh Jain | LinkedIn), a thought leader in Digital Twins and AI, is working closely with Xavor to adopt the P4 model for digital twins and provide exceptional healthcare to patients worldwide.

Xavor’s experience with leading healthcare and med tech companies adopting AI ML solutions gives us the opportunity to improve everyone’s quality of life through digital twin technology.