Three perspectives, one goal: Better healthcare for everyone
Modern medicine faces an unprecedented opportunity: by sharing health data, we can understand diseases better, detect them earlier, and treat them more effectively. But what does that mean in practice? Three voices from research show why your data donation is so valuable.
Basic research: Decoding molecular mysteries
“In precision medicine, combining multi-omics with clinical data helps reveal complex patterns and relationships. It delivers insights that enable better diagnostics, improved prevention, and more individualized treatments”, says PD Dr. Katja Bärenfaller, Group Leader at the Swiss Institute of Allergy and Asthma Research (SIAF) in Davos.
The journey to new therapies begins in basic research labs. Scientists study how diseases arise at the cellular and genetic level. But a single dataset is not enough—only by analyzing many different health datasets do patterns and relationships emerge. Your data donation helps decipher the causes of diseases like cancer, Alzheimer’s, or diabetes. This knowledge forms the basis for medicines and treatments that target disease where it begins. Because patients made their data available, Dr. Bärenfaller, together with other research groups, was able to show that symptoms related to joint prostheses are often due to hypersensitivity to prosthesis components such as nickel or vanadium. (More in the publication)
Data science: When technology saves lives
“Artificial intelligence can only be as good as the data it learns from. High-quality health data are the foundation for algorithms that support physicians with complex diagnoses”, says Prof. Douglas Teodoro, Assistant Professor at the University of Geneva and Head of the Data Science for Digital Health group.
Modern computer models can analyze vast amounts of data and detect patterns that remain invisible to the human eye. But these algorithms first need to learn—from real health data. The more high-quality data available, the more accurate the predictions become. The result: doctors receive valuable diagnostic support, high-risk patients are identified earlier, and treatments can be tailored to the individual. Your data train the systems of tomorrow. Information on adverse drug reactions together with clinical context—such as dosage, route of administration, and patient demographics—has enormous potential to improve side-effect predictions. Researchers around Prof. Teodoro showed that training AI models with these data can improve their accuracy by up to 38% compared to models that use only drug structure. (More in the publication)
Clinical medicine: From lab to bedside
“Health data are the key to precision medicine. They help us find the optimal treatment for each patient—individual and targeted”, says Prof. Matthias Baumgartner, Director of Research & Education at the University Children’s Hospital Zurich.
In daily practice, physicians aim to find the best therapy for each person. Yet not every treatment works equally well for everyone. Health data reveal which therapy is most successful for which patients. This is how personalized medicine emerges: tailored treatments that fit your individual situation. Your data donation helps ensure that future patients—perhaps even you—benefit from more precise and effective therapies. By building a nationwide pediatric network (SwissPedHealth), health data from children and adolescents will be made securely and uniformly usable across clinics. Together with colleagues across Switzerland, Prof. Baumgartner aims to create a sustainable, scalable infrastructure—for better, more targeted care. (Find more information here)
Shaping the future together
Whether in basic research, data analysis, or clinical practice—health data are the key to medical progress. Your data donation is an act of solidarity that has an impact far beyond the moment.
Sources
1. Tamara El Saadany et al. Clinical Presentation and Causes of Prosthesis-Related Hypersensitivity Reactions: An Analysis of 225 Patients. Immunology and Allergy. 27 August 2025. DOI: 10.1159/000547103
2. Anthony Yazdani et al. An Evaluation Benchmark for Adverse Drug Event Prediction from Clinical Trial Results. Scientific Data. 11 March 2025. DOI: 10.1038/s41597-025-04718-1
3. Rebeca Mozun et al. Paediatric Personalized Research Network Switzerland (SwissPedHealth): A Joint Paediatric National Data Stream. MedRxiv. 24 July 2024 DOI: 10.1101/2024.07.24.24310922
(Image: Google DeepMind / Unsplash)
