What Is The Potential For Digital Twins In Healthcare?

Digital twins are virtual representations of an object or system that spans its lifecycle, is updated from real-time data, and use simulation, machine learning and reasoning to help decision-making (IBM). By using digital twins to model a person, you can use technologies like natural language processing (NLP) to better understand data and uncover other useful insights that will help improve use cases from customer experience to patient care. Digital twins can be useful in synthesizing this information to provide actionable insights. Kaiser Permanente uses digital twins through a system that improves patient flow within a hospital. In another instance, Roche uses digital twins to help securely integrate and display relevant aggregated data about cancer patients into a single, holistic patient timeline. By building a digital twin, you can compare an individual to other patients – similar in clinically important ways – to see if there are genomic similarities and how certain treatments have impacted them. Creating a digital twin to anticipate patient needs and the length of their stay can be very valuable. For example, creating the digital twin of a patient’s heart enables a doctor to see exactly what’s going on — whether there is scarring from previous surgeries or an abnormality that needs to be inspected further — and make better decisions before an operation, rather than during.