Enabling Personalized Care through Digital Twin Technology
The ineffectiveness of standardized and approved medications on patients is a cause of concern globally.
Furthermore, the percentage of patients for whom medications are ineffective ranges from 38-75% for varying conditions from depression to osteoporosis.
The main cause behind drugs being ineffective is the very specific genetic makeup of every individual. The latter is so different and their interaction so unique that therapies for an average patient may not be well-suited to the actual patient – not to mention how drug testing is not representative.
Global researchers and healthcare practitioners have realized the power of digital twins to boost precision medicine – an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person to maximize efficacy and efficiency.
A digital twin is a virtual model or simulation of any object, process, or system that is created using real-world data to learn more about its real-world counterpart. In the healthcare metaverse, the patient’s digital twin is the patient themself.
The ‘one-size-fits-all’ approach to disease treatment fails to consider differences between individual treatments, reducing chances of survival and increasing patients’ exposure to adverse effects.
Designing the UX of diagnostic software using digital twin technology to customize diagnosis and treatment for patients by integrating patient-centric data analysis into clinical decision-making.
The healthcare industry is constantly striving to enhance patient outcomes, reduce operating costs, and address unforeseen medical crises effectively. We wanted to create a conceptual diagnostic tool using quick, iterative testing to collect real-time data through sensors and reflect them into digital devices – a novel approach for healthcare diagnostics.
We followed the Minimum Viable Product (MVP) design approach to create a viable, solution in a short span. It is an iterative process based on constant user feedback and remains user-focused throughout. It aims to tackle problems or address needs in unique ways. It is an opportunity to set a benchmark in the industry.
Research methods used
- User interviews
- Process mapping
- Case studies and report referrals
The module is designed to capture continuous data from the individual about various vitals, medical conditions, and responses to the drug, therapy, and surrounding ecosystem. Historic and real-time data of each patient helps the ML algorithm to predict future health conditions and analysis of drug trials. This module leverages a large amount of rich data from various IoT devices and uses AI-powered models to develop more personalized and improved solutions.
The module captures continuous data from an individual patient about various vitals, medical conditions, and healthcare history to create a digital twin.
It runs an ML algorithm that suggests the best treatment options for trial on the digital twin.
The system leverages the gathered data and runs trials on the digital twin to determine the right therapy and predict the outcome of a specific procedure.
It comes up with a comparative analysis of the best possible treatment and suggests an optimally effective solution based on precision medicine.
With clinical evidence and real-world data, this digital twin module is designed to create simulations of new treatments and bring lifesaving innovations at a 24% faster rate.
In the coming future, digital twin simulation software could be used to analyze the progression of neurogenerative ailments such as Alzheimer’s and Parkinson’s, better understand treatment options and accelerate trial timelines.
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