The Expertisecentrum Zorgalgoritmen develops machine‑learning algorithms in co‑creation with clinicians, using each hospital’s own data to generate real‑time predictions for diagnosis, prognosis and treatment decisions. Their federated learning platform trains models locally to preserve patient privacy, integrates the AI outputs into existing clinical systems, and provides end‑to‑end support—including validation, CE‑marking and staff training—to help hospitals adopt AI‑driven decision support safely and efficiently.
Funding
Funding not disclosed
Founders
Product
Problem
Healthcare providers must make frequent decisions about diagnosis, disease prognosis, and treatment protocols, often under time pressure and with limited access to advanced analytics. Hospital data are abundant but remain underutilized for predictive modeling, leading to inefficiencies, unnecessary care, and longer hospital stays.
Solution
The Expertisecentrum Zorgalgoritmen (EZA) collaborates with clinicians to develop machine‑learning algorithms that generate actionable predictions from each hospital’s own data. Using a federated learning approach, models are trained locally on hospital datasets and aggregated centrally, ensuring patient data never leave the institution. The resulting algorithms are embedded into existing clinical applications, delivering real‑time decision support at the point of care. EZA also provides a software platform for model development, technical integration, and staff training, enabling hospitals to adopt AI responsibly and cost‑effectively while maintaining regulatory compliance such as CE‑marking.
Target Audience
Primary customers are hospitals and healthcare organizations within the SAZ network that seek AI‑driven decision support tools for physicians, nurses, and allied health professionals.
Features
- Federated learning framework that trains predictive models on local hospital data while preserving data privacy
- Customizable software platform for building, validating, and deploying machine‑learning algorithms in clinical workflows
- Seamless integration of AI predictions into existing hospital information systems and bedside applications
- End‑to‑end support for technical implementation, clinical validation, and staff education on AI use
- CE‑marked algorithmic solutions to meet regulatory requirements for medical device software
- Collaborative co‑creation process involving clinicians to ensure models address real‑world care challenges