Digital Twin

What is a digital patient twin?

In healthcare, the digital patient twin is being researched and developed for specific organs like the heart and liver. But what, in fact, is a digital patient twin? Let's look into what this could be, and which applications contribute to its concept.

10 min
Peter Aulbach
Published on March 21, 2023

There are several concepts of digital twins currently being used in healthcare. One central approach is digital patient twins: With their help, doctors are increasingly able to better predict how a person ages, when illnesses appear, what course they take, and what the most effective treatment is. 

<p>Could a digital patient twin help sick people get back to normal life as soon as possible? Or could it even prevent people from getting sick in the first place? This sounds like a big dream from today’s perspective. See how it could actually work:</p>
<p>These simulations of a patient’s state of health are based on biophysiological data models created with algorithms. In principle, the digital patient twin might process recordable data received from a wide variety of sources without having to store the data in a single place – and ideally for the patient’s lifetime.</p><p>The digital patient twin would collect an individual’s existing health information in real-time and continually compares this data with the results from population studies, data on specific clinical pathologies, the course of specific diseases, and information on medications, diagnostics, and therapies used for other affected individuals.</p><p>Taking into account collected evidence, paired with clinical guidelines and economic considerations, the twin could enable doctors creating a holistic, individual, and comprehensive preventive or treatment regime.</p>
<p>For practical implementation of a digital patient twin, there are four essential requirements:&nbsp;</p><ol><li>The hospitals or healthcare practices must be sufficiently digitally networked.&nbsp;</li><li>The data must be structured and annotated.&nbsp;</li><li>Patients must be able to decide about the use of their data at all times.&nbsp;</li><li>Medical professionals must have access to the information processed in the digital patient twin and be able to use this digital interface in everyday clinical practice.&nbsp;</li></ol><p>There is still a long way to go before a digital patient twin could be fully realized in healthcare. But the technology itself is no longer science fiction. Powerful AI-based solutions that will most likely contribute to the concept of a digital patient twin are already in use:</p>
<p>The basis for using the digital patient twin is a running digital infrastructure. Although the technology already exists, it will take many years to translate these concepts into clinical applications. So, it is especially important to pave the way in hospitals today and continuously implement partial solutions as they become available.</p>
<p>The added value of the digital patient twin concept for hospitals will come from the multitude of possible applications in medicine and administration that optimize the healthcare pathway. This technology will be indispensable in clinical routines for translating the growing amount of data into decision-relevant knowledge. Ultimately, it is expected to ensure that the process of care before, during, and after a clinical treatment will be even more individualized and precise.</p>

Learn more about digital patient twins

Patient twinning – the future of healthcare
Healthcare Perspectives
Patient twinning – the future of healthcare
Find out what a patient twin is, how it can be created, and what benefits it could offer both patients and medical practitioners. You’ll also learn more about the cloud-based software, Noona, which can be seen as a first step towards a disease-focused version of the digital twin, and how it is used by cancer patients as their 24/7 companion on their journey.
<p>Sources: Lou B, Doken S, Zhuang T, Wingerter D, Gidwani M, Mistry N, Ladic L, Kamen A, Abazeed ME (2019) An image-based deep learning framework for individualising radiotherapy dose: a retrospective analysis of outcome prediction. The Lancet Digital Health 1, 136-147.</p>

By Peter Aulbach