Two Use Cases of Digital Twins in Healthcare
A digital twin is an exact virtual representation of an object or system that uses real-time data simulation, machine learning, and artificial intelligence to help decision-making. The represented object could be a person, system, process, or device. Digital twin technology can be used in healthcare to optimize performance, products, costs, and patient care. The digital representations are generated through multiple sources including from patients, hospitals, labs, etc. The insight gathered from digital twin technology leads to innovations that minimize risk and improve healthcare. Generally, healthcare use cases of digital twins will either improve personal care or operational efficiency within a healthcare setting.
Importance of Digital Twins in Healthcare
Predicting the impacts of experimental treatments or use of new devices can potentially save lives without putting patients at risk. Prematurely using new products or procedures on patients can be catastrophic. Testing is essential for new care treatments and products, precisely where data and insight from digital twins comes into play. Digital twins help to provide access to real-world data and minimize possible risks to patients.
Improving patient care and research are two benefits of using digital twin technology in healthcare. Data can be used to drive action in relation to general health care services and inform research associated with pharmaceuticals and medical devices. Digital twin solutions offer healthcare providers and pharmaceutical companies the ability to model genetic data, biological characteristics, and patient lifestyles in order for healthcare companies to provide highly personalized care.
With a realistic representation of patients, physicians can experiment with different care delivery approaches and train for complex and invasive surgical procedures. Digital twin solutions enable health care providers to have a better understanding of the unique qualities of each patient and how they may react to care, medicines, and devices.
Digital Twin Use Cases
Within the healthcare and medical device fields respectively, some companies are using innovation labs to experiment with digital twin services. Let’s take a look at two digital twin uses cases, one dedicated to improving long-term personal care and the other helping to improve operational efficiency relating to supply chain and performance.
1. Improving Personal Care
In the first digital twins in healthcare use case, digital twins and patient telemetry are being used in combination with medical records to assess patient risk for life-threatening conditions and persistent problems impacting patient comfort. Personalized, predictive remote care can elevate the ability of physicians to care for and enrich the lives of their patients through the remote monitoring of their vital statistics 24×7.
The challenge presented was building remote monitoring for patient vital statistics. To ensure performance and get the intended result desired, it was necessary to overcome technology challenges including the development of a mobile app, creating a framework for collecting data, transmitting real-time data, developing telemetry, and handling the de-identification of patient data.
For this company to overcome their challenges, five core areas, including a companion mobile app, V&V and production support, data analytics and science, cloud and DevOps, and pharma partner SDK became the central focus to provide a comprehensive solution.
Key outcomes:
- Dramatically faster product cycle time through fail fast approach
- Ability to monitor key device parameters and take proactive steps for improved user satisfaction
- Scalable, high-performance cloud architecture through strong GCP cloud engineering and DevOps practices
2. Improving operational efficiency
In the second case, a medical device company used the lab to improve its efficiency, testing ways to optimize its supply chain using digital twins. It tested changes to distribution transportation and maintenance to see how it impacted performance. The advantage to using this technology instead of testing in a real-life environment is the ability to avoid disruption. Testing and changing scenarios in a real-life environment could cause chaos for suppliers and customers, but those risks are eliminated with digital twin technology.
Innovation labs enable participants to refine and tune aspects of their initiatives to ensure they can attain the expected results. For each of the use cases mentioned, one of the key aspects was data connectivity and the ability to inform the digital twins with real-time data from the production environment. From an operational standpoint, areas that can be improved include models for care, staffing, and overall strategies.
Elevating Healthcare with Digital Twin Technology
Better care and improved operational efficiency are both initiatives the healthcare industry is continually striving for. However, innovation can often be unpredictable—an unfavorable characteristic within the medical field. So how can healthcare organizations pursue digital transformation while avoiding risks? The answer: digital twins. This technology can unlock the answer to better patient care and business effectiveness without any risk of real-world experimentation. Digital twins may just be the crystal ball caregivers have been looking for.
Apexon’s Digital Engineering services help companies to accelerate innovation, improve business operations, boost productivity, and increase their levels of automation. Apexon provides end-to-end application development and engineering. Through automation development, Apexon can bring your digital ideas to life faster while minimizing your risk.
In relation to digital twins, Apexon’s expertise helps enterprises to increase personalization, speed cycle time and reduce risk. If you’re interested in learning more about how we use Digital twins, check out Apexon’s Application Development services or get in touch with us directly using the form below.
Also read: Enhancing GenAI Applications with Azure OpenAI Function Calling