Currently, the ability to predict potential future complications that may result in further hospital re-admissions post-stroke is limited. In response, the EU Horizon-funded research project TRUSTroke is developing an advanced technology to help healthcare professionals predict both short- and long-term outcomes of an ischemic stroke.
Using Artificial Intelligence (AI), the project will enable healthcare professionals to analyse large amounts of information to find patterns in recovery and predict future health risks. This will ultimately enable increased personalisation of treatment and effective long-term stroke management.
As part of the project’s deliverables, a Patient Experience (PX) research team is collaborating with stroke survivors from hospitals across three European countries. They are gathering insights into the challenges patients face post-stroke and working to optimize Nora, a health app, based on their needs.
Once developed, the app will allow patients to input their health information and get answers to simple questions, helping them stay connected with their healthcare professionals. By tracking their own health over time, stroke survivors will gain a clearer picture of their progress, while healthcare professionals will have better insights into their recovery.
“Because this app is built with input from stroke survivors, it is designed to be simple and accessible,” explains Carolina Lauzen, UX Designer at Nacar Design. “It will help patients stay on track with their treatment plans and make doctor visits more effective by providing a clear overview of their progress.”
Arlene Wilkie, Director General of the Stroke Alliance for Europe, says “One of the biggest concerns for stroke survivors is not knowing what will happen in the future. This new technology, designed with their input, will help healthcare professionals and stroke survivors work together on long-term recovery plans.”
To learn more about the project:
Click to view video about the app
Or click to visit the TRUSTroke website
Or contact SAFE on research@safestroke.eu
This project is supported by Horizon Europe under grant agreement Nº101080564
