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The Proactive P4 Healthcare Model for Advanced Medical Services

Digital twin technology is being used by healthcare professionals to advance the quality of life through pre-emptive patient care. The modern implementation of digital twins in healthcare draws on AI ML solutions already integrated in modern healthcare services.

Digital twin is a virtual replica of a real object, function or even a place which is then used to run through simulations for a situational analysis. By integrating AI ML functionalities and cloud applications, digital twins can utilize real time data and make accurate predictions regarding health outcomes for individuals. 

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 developed to standardize the use of digital twins in health care. The digital twin will act out 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 or the possibility of disease reversion such as in cancer.

The predictive features of the digital twin are also able to identify lifestyle patterns and alert 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 and 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 alerts doctors to the reversion of his cancer. 

Preventive:

Since the digital twin can simulate and predict outcomes with precision, individuals can shift from treatment of diseases to maintaining healthy lifestyles. The digital twin incorporates vital data for patients including early health markers.

It uses information about the early health markers, assesses the risks and reports which harmful behavior patients should avoid in order to lead a healthy life.

Healthcare professionals employ Machine Learning solutions to test the efficacy of preventive plans for particular patients in order to advise them on the best actions to maintain their wellbeing. 

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

The twin proves empirically how Joe can cut down on cigarettes, add some exercise and live a long and healthy life. 

Participatory:

The digital twin through the P4 healthcare model is interactive and easier to understand for patients. The digital twin 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 Twin becomes a powerful healthcare tool when integrated with cloud and AI ML capabilities. To create a personalized model for each patient, the digital twin assimilates data collected from 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 makes it possible to fetch 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 is processed along with his medical records. The personalized digital twin interacts and informs Joe in real time what lifestyle habits he should adopt and maintain to keep himself healthy.

Joe’s data is stored in the form of a health index, a score out of 100 to show him how healthy his day has been, therefore reinforcing 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 the Digital Twin and AI field, is closely working with Xavor to adopt the P4 model for digital twins and provide exceptional healthcare to patients all over the world.

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

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Muhammad Moin ud din

Muhammad Moin ud din

Muhammad Moin Ud Din is a Content Strategist at Xavor Corporation where he plans, creates and manages content. His favorite writing and conversation topics are macroeconomics, physical and mental health, and comic book movies. In his free time, Moin does voice acting and promotes professional wrestling in Pakistan.