A digital twin in healthcare is a virtual copy of a real thing, like a patient’s organ, body system, or even large groups of patients. Unlike regular static models or 3D pictures, digital twins change constantly by using real-time data. They show how a patient’s health might change over time.
Natalia Trayanova, a professor of Biomedical Engineering at Johns Hopkins University, explains that a digital twin models not only the shape of an organ but also how it changes and interacts inside the body. For example, her team made digital twins of patients’ hearts that simulate electrical signals and disease changes using MRI and PET scan data. This helps predict problems like irregular heartbeats and guides treatments such as catheter ablation. The system used at Johns Hopkins was the first in heart care to get FDA approval. It was tested in a clinical trial, marking a key step toward using digital twins in normal medical care.
Digital twins are useful because they can forecast how a disease might get worse for a patient and suggest personalized treatments based on those forecasts. This changes healthcare from a “one-size-fits-all” approach to one that fits each person’s needs. This helps with both preventing and treating illnesses.
For healthcare managers and IT staff, digital twins offer several benefits:
Digital twins can also be used for whole populations. For example, the Cleveland Clinic uses digital twins to study health in neighborhoods by combining medical records with social and environmental data. This helps find health differences caused by social factors, like how life expectancy can vary by 25 years depending on where a person lives. This shows how digital twins give useful data beyond single patients to help improve community health.
Using digital twin technology can be very helpful in U.S. hospitals, clinics, and outpatient centers. The need for care tailored to each person is growing as the population becomes larger and older.
Though digital twins offer many benefits, using them in healthcare brings challenges that administrators and IT leaders must handle carefully.
Experts like Alexandre Vallée stress that strict rules and regulations should be followed to protect patient rights while allowing innovation. Organizations using this tech need to be clear and careful to build trust with patients and regulators.
Artificial Intelligence (AI) is important for building and using digital twins. Healthcare managers and IT staff need to understand how AI and automation work with digital twins to improve efficiency and patient care.
Machine learning looks at lots of data from electronic health records, images, wearable devices, genes, and the environment. AI helps digital twins stay updated with new patient information and guess future health outcomes as conditions change.
At Johns Hopkins, AI helps build heart digital twins by analyzing images and picking patients for certain treatments. Fast and exact simulations help doctors choose the best methods, which may reduce unnecessary or risky procedures.
Adding digital twins into clinical workflows helps automate important routine tasks like:
Places like the University of Nebraska Medical Center’s iEXCEL Lab show how AI-driven tools support tele-simulation and tele-mentoring. Digital twins help healthcare workers in remote or underserved areas get on-time training and expert advice. This lowers patient transfers and improves care quality in rural regions.
Automation also helps keep quality consistent by checking the success of training and patient care plans using real-world data from digital twin systems.
Knowing how digital twins affect healthcare helps managers see their value for normal and advanced care:
As more providers use digital twins, sharing data and working together become more important. Cloud platforms help combine many types of data and 3D models, making digital twins easier to use across healthcare systems.
In the U.S., healthcare administrators and IT teams play a big role in safely adopting digital twin technology. Their tasks include:
As digital twins and AI automation grow, medical centers that make smart choices now will be ready to handle future challenges and improve care for patients across the country.
Bringing digital twins into U.S. healthcare offers new ways to provide precise and personalized patient care. By creating virtual health profiles and predicting disease changes, digital twins give data that helps doctors make better decisions. When combined with AI-driven workflow automation, they can improve how healthcare is given, lower unneeded hospital visits, and support remote care, which is especially important for rural and underserved areas.
Medical practice managers, owners, and IT staff who learn about and use digital twin technology will help make healthcare safer, more efficient, and more personal throughout the country.
The primary goal of the iEXCEL initiative is to improve human performance and effectiveness in healthcare through advanced simulation training and education utilizing emerging technologies like XR and AI.
iEXCEL leverages AI by using it for automating content creation, enhancing data-driven decision-making, and developing intelligent digital twins that analyze patient care and training effectiveness.
Emerging technologies allow rural clinics to access real-time training and support, reducing patient transfers to tertiary hospitals and enhancing healthcare delivery through tele-simulation and tele-mentoring.
The lab includes specialty areas for performance analysis, quality assurance, AI integration, and immersive experience creation, working collaboratively to innovate in medical training.
Tele-simulation enables healthcare professionals to receive ‘just-in-time’ training remotely, ensuring they stay updated on best practices without the need for travel.
Digital twins can provide real-time interaction with data, predicting outcomes based on various parameters and ultimately improving patient care and training.
Continuous evaluation ensures that emerging technologies are relevant, reliable, and effective, thus maintaining high standards in healthcare training and patient outcomes.
iEXCEL tests new tools in secure environments, involving potential users in the evaluation process to assess usability and relevance.
Democratizing access helps bridge the gap between urban and rural healthcare, providing equal training opportunities and improving overall healthcare quality.
The lab is structured around distinct engines focused on performance, quality, intelligence, and experience, facilitating collaboration and rapid development of innovative solutions.
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