Among the emerging technologies in the healthcare sector is Digital Health Twin (DHT). The use of digital twins in healthcare is becoming increasingly popular, assisting doctors and patients in viewing the same patient from various perspectives, which can save time and money.
In the future of healthcare, patients will have their own Digital Health Twin (DHT). A digital twin is a virtual representation of a physical object, process, or system. In the case of healthcare, a digital twin would be a virtual representation of a patient.
The digital twin would be created by collecting data from a variety of sources, including medical records, wearable devices, and environmental sensors. This data would be used to create a detailed model of the patient’s physical and mental health. Artificial intelligence (AI) can help digital twins be more efficient by providing insights that go beyond what real-world sensors can provide. It is also capable of making future predictions. Based on the data it receives, AI can independently decide which tests to run, and it can then predict which actions would result in the desired outcomes — all of this happens automatically. Furthermore, algorithms can detect any abnormal data from sensors quickly.
The digital twin would be used to track the patient’s health over time and to identify any potential problems. It could also be used to simulate different treatment options and to predict the outcome of each option. This information would help doctors to make more informed decisions about the patient’s care. It could also help patients to take a more active role in their own healthcare.
DHTs have the potential to revolutionize the way we deliver healthcare. They could help us to prevent diseases, diagnose them earlier, and provide more effective treatments.
Digital Health Twins require New Technologies and Processes
Here are the essentials checklists:
- Connected infrastructure: Creating digital health twins necessitates the collection of a large amount of data from various sources. For data collection, an investment in a connected infrastructure comprised of sensors and IoT devices, application programme interfaces (APIs), and similar technologies is required.
- Modeling and analytics technologies: AI, machine learning, predictive analytics, and 3D modeling are all critical components of developing effective digital health twins and accurate data models.
- A data lake: A common data source capable of storing large amounts of structured, semi structured, and unstructured data is required for digital health twins. As a result, it is prudent for organizations to invest in the development of data lakes that enable rapid data ingestion and advanced analytics.
- Data Scientists: Digital twins are most often built by data scientists who are skilled at researching the physics of the objects they’re interested in emulating. Although digital twin building software has made the process much easier, it’s still a good idea to assign the creation of digital twins to experts who are familiar and have worked with large datasets and complex algorithms.
Here are some of the benefits of using Digital Health Twins
- Improved patient care: DHTs can help doctors to provide more personalized care to their patients. By having a detailed understanding of the patient’s health, doctors can make better decisions about treatment.
- Prevention: DHTs can help to prevent diseases by identifying potential problems early on. For example, if a digital twin shows that a patient is at risk for heart disease, the doctor can intervene early and prevent the disease from developing.
- Earlier diagnosis: DHTs can help to diagnose diseases earlier, which can lead to better outcomes. For example, if a digital twin shows that a patient has a tumor, the doctor can diagnose the tumor earlier and begin treatment sooner.
- More effective treatments: DHTs can help to develop more effective treatments for diseases. By understanding how a disease affects the patient’s body, doctors can develop treatments that are more likely to be successful.
Examples of how Digital Health Twins are being used in healthcare today
- A hospital in the United Kingdom is using DHTs to track the progress of patients with chronic diseases. The hospital has found that DHTs can help to identify patients who are at risk for complications, and to intervene early to prevent these complications from occurring.
- A pharmaceutical company is using DHTs to develop new treatments for cancer. The company is using DHTs to identify patients who are most likely to benefit from a particular treatment, and to track the effectiveness of the treatment over time.
- A health insurance company is using DHTs to identify patients who are at risk for high-cost medical events. The company is using DHTs to contact these patients and offer them preventive services, such as smoking cessation programmes or weight loss programmes.
These are just a few examples of how DHTs are being used in healthcare today. As the technology continues to mature, DHTs are likely to become an increasingly important tool for doctors and patients.
Some challenges that need to be addressed before digital health twins can be widely adopted
- Data privacy: One of the biggest challenges with Digital Health Twins is data privacy. Patients need to be confident that their data will be kept private and secure.
- Cost: Digital Health Twins can be expensive to develop and maintain. This could be a barrier to adoption in some cases.
- Acceptance: Patients need to be willing to use the new technology, but his could be a challenge, especially for older patients who are not comfortable with it.
Digital Health Twin Technology is Evolving
There are many factors that will drive this evolution in healthcare over the next few years, such as:
Advancements in Trusted AI: The success of DHT in healthcare is dependent on users’ ability to trust the information presented to them. That means the AI powering the models must be reliable. This will necessitate a focus on trusted AI, the ability to see and understand the reasoning behind the digital twin’s predictions and diagnoses.
Focusing on Data Stewardship and Security: The handling of patient data will be a major topic of discussion as well. To protect patient information and maintain data integrity, organizations will need to establish their own rules and outline practices for data stewardship and security.
The Proliferation of DHTs As-a-Service: The market for outsourced digital twin services provided by major technology providers will grow further. This will broaden the appeal of digital health twins and make them more accessible to healthcare organizations of all sizes.
Creating Digital Twins of Healthcare Facilities: Enterprise wide, DHT of healthcare facilities will be created by organizations, resulting in significant operational benefits. Hospital digital health twins, for example, will be used to identify bed shortages, manage staff schedules, detect germ spread, and more.
Digitizing the Human Body: While technology exists to digitize parts of the human anatomy, creating an accurate digital twin of a human body is still on the horizon. Because creating a digital replica of a body necessitates extensive virtual simulation. However, progress is being made in this area. Within the next few years, doctors will most likely be able to identify previously unknown illnesses, predict how the body will react to certain treatments, and even identify and predict brain aneurysms.
More Data Collection for Better-Trained Models: The collection of massive amounts of data required to support digital twins will be driven by a steady increase in the number of IoT-enabled devices and endpoints. To improve treatment and outcomes, digital health twins will be able to draw information from a wide range of sources, including scans and medical records, as well as tests such as ECGs.
Continuous Interface Improvements: User interfaces will continue to improve, making it easier for non-technical users to navigate DHTs and distinguish findings and intelligence.
Despite these challenges, Digital Health Twins have the potential to make a real difference in the way we deliver healthcare. As the technology continues to develop, we can expect to see even more benefits from DHTs in the future.
An article by: Anoop Ravindran– Technology Partnership Specialist
(6) Digital Twin in Healthcare: What It Is, What It Does | LinkedIn