Sometimes the most important lessons in medicine come when you least expect them. Not from books, or training, or years of practice, but from the people you are trying to help.
Imagine a patient named Maria, who has been suffering from depression for a while.
Maria’s practitioner checks the data from her digital twin and notices that the little alerts that were set up to monitor subtle changes caught almost everything that the practitioner did not.
Although Maria looked seemingly pleasing on the surface, the data from the digital twin suggested something was off and that Maria needed serious help for her mental health condition.
Understanding Digital Twins
A digital twin in the context of medicine is simply a virtual model of a physical object. A digital twin could either represent a person’s organs or an entire population. It could way of tracking someone’s health to diagnose and predict the outcome of treatment virtually and provide a fuller picture than a 10-minute appointment ever can.
Digital twins are of particular significance within the emergency and intensive care units to help practitioners and trainees to better understand a patient’s health progression. A digital twin in medicine serves as a clinical decision support system, reducing cognitive overload and thereby improving diagnosis, treatment accuracy, and outcomes. A digital twin acts as a co-pilot for surgeons and clinicians to test and refine decisions in a virtual space without risking real patient safety. A digital twin can be effective in noticing minute details of depression, like when you stop replying to texts, stop going for walks, or sleep at odd hours, or how anxiety ramps up your pulse or shortens your breathing before your mind even registers it.
The digital twin does not judge. It just listens quietly and constantly (Eftimie et al., 2023).
Why is Digital Twin Important in Managing Mental Health Conditions
Mental health convinces people they are okay when they are not. Patients forget what they’ve felt, or they hide it because they don’t want to be a burden.
People paint a picture of being all fine and good, while their families are on the phone, terrified. Not because they are being dishonest, but because they genuinely can’t tell that something is wrong. Depression, anxiety, PTSD, they all mess with your memory, your sense of time, and your ability to reflect clearly.
When someone’s drowning in panic or spiralling into depression, they cannot always spot the build-up; however, the data often reveals a pattern subtle at first but steady (Zhang et al., 2024).
Sarah’s Story
Consider a patient, Sarah, struggling for years with anxiety and PTSD. Sarah is on medications and therapy sessions, but nothing seems to stop the constant feeling of chaos. Her panic attacks would strike out of nowhere, leaving her exhausted and afraid to live fully.
“I don’t even know what normal feels like anymore,” she said.
Out of options, her clinician tried using a digital twin, a way to quietly monitor what was happening between sessions. At the time, the clinician thought maybe it would help them spot a few sleep issues or bad days, but eventually, what they observed changed everything.
About six weeks in, they started noticing a pattern. The panic attacks didn’t come out of nowhere at all. They always followed the same lead-up:
Two nights of disturbed sleep
A higher resting heart rate
Skipping meals and social outings
Then, a panic episode
She had never noticed it. Neither had the clinician, but once they saw the pattern, they could prepare. Sarah’s therapist messaged her when her sleep data flagged two bad nights in a row. She could slow down, talk things through, and take a break before things got overwhelming.
Coming to the privacy aspect. This kind of data is deeply personal. What if it ends up in the wrong hands? What if insurance companies use it the wrong way? What if people are judged by things they cannot control?
What if the data’s wrong? Or worse, what if we start depending on numbers so much that we forget how to listen?
We cannot reduce people to charts or graphs. A digital twin cannot replace a real connection, but when used well, it can make that connection stronger. It gives us a way to understand patients in between the moments they sit in the facility. It gives the care providers a way to help before it is too late (Huang et al., 2022) (Delerm and Pilottin, 2024).
Critical Information for Healthcare Professionals
This is not about becoming tech experts; rather, it is about being mindful and present to act more quickly and to care more deeply and personally (on behalf of the Swedish Digital Twin Consortium et al., 2020).
It is also about being humble and learning from the people we treat, not just about their stories, but about the blind spots in our care. The digital twin does not teach how to be a better doctor; patients do. The tech just helps the care providers hear what they were trying to say.
When Things Feel Overwhelming
If you are reading this and you are the one dealing with a mental health condition, I want to tell you this: it is not your fault if you cannot see the warning signs. It is not a failure if you do not catch your patterns. That is the illness talking.
A digital twin will not cure you, but it might help you understand yourself better. It might show you what your brain is hiding. It might give you a way to explain to your loved ones or your doctor what you cannot always find the words for.
You are not broken, nor are you alone. There are tools now that can help, tools that work with you, not on you. Tools that remind you: you are not the problem, and with the right support, you don’t have to do it alone.
Conclusion
We all want a future where we stop waiting until people hit rock bottom. Where we catch changes before, they spiral. Where we treat mental health with the same urgency, the same attention, and the same science that we give physical health.
We want teenagers to know their moods are not just “teen moods.” We want people like Sarah to feel safe making plans. We want people like Maria to be heard, even when they cannot speak.
We are not there yet, but if we believe we can get there (Kamel Boulos and Zhang, 2021).
References
- Delerm, F., Pilottin, A., 2024. Double edged tech: navigating the public health and legal challenges of digital twin technology. Eur. J. Public Health 34, ckae144.1510. https://doi.org/10.1093/eurpub/ckae144.1510
- Eftimie, R., Mavrodin, A., Bordas, S.P.A., 2023. From digital control to digital twins in medicine: A brief review and future perspectives, in: Advances in Applied Mechanics. Elsevier, pp. 323–368. https://doi.org/10.1016/bs.aams.2022.09.001
- Huang, P.-H., Kim, K.-H., Schermer, M., 2022. Ethical Issues of Digital Twins for Personalized Health Care Service: Preliminary Mapping Study. J. Med. Internet Res. 24, e33081. https://doi.org/10.2196/33081
- Kamel Boulos, M.N., Zhang, P., 2021. Digital Twins: From Personalised Medicine to Precision Public Health. J. Pers. Med. 11, 745. https://doi.org/10.3390/jpm11080745
- on behalf of the Swedish Digital Twin Consortium, Björnsson, B., Borrebaeck, C., Elander, N., Gasslander, T., Gawel, D.R., Gustafsson, M., Jörnsten, R., Lee, E.J., Li, X., Lilja, S., Martínez-Enguita, D., Matussek, A., Sandström, P., Schäfer, S., Stenmarker, M., Sun, X.F., Sysoev, O., Zhang, H., Benson, M., 2020. Digital twins to personalize medicine. Genome Med. 12, 4. https://doi.org/10.1186/s13073-019-0701-3
- Zhang, K., Zhou, H.-Y., Baptista-Hon, D.T., Gao, Y., Liu, X., Oermann, E., Xu, S., Jin, S., Zhang, J., Sun, Z., Yin, Y., Razmi, R.M., Loupy, A., Beck, S., Qu, J., Wu, J., 2024. Concepts and applications of digital twins in healthcare and medicine. Patterns 5, 101028. https://doi.org/10.1016/j.patter.2024.101028