High-quality emergency departments (EDs) are essential for optimal patient care. To improve their clinical and organisational performance, EDs must be assessed using validated criteria, which requires reliable data collection. However, the fast-paced emergency setting, large patient volume, and chronic staff shortages make dedicated data gathering impractical.
The only viable solution is automatic extraction of data from electronic health records (EHRs). While some EHR information is structured (e.g. lab results), much of the relevant content is found in free text notes written by clinicians and nurses, making extraction complex.
To address this, the eCREAM project was launched, involving partners from seven European countries and more than 30 EDs. Its main goal is to develop artificial intelligence–based natural language processing (NLP) tools capable of extracting clinical information from different sources to create robust databases. eCREAM will also design a new EHR prioritising accurate, trustworthy data collection without increasing staff workload.
Two use cases will test its real-world application: assessing EDs’ propensity to hospitalise patients, and creating real-time dashboards to inform clinicians, citizens, and policymakers about ED status, supporting better decision-making.
The project’s impact is expected to be substantial. By sharing unique data, eCREAM will contribute to the European Health Data Space, support researchers with structured datasets, and advance NLP methods for the medical domain. Its dashboards will empower users: patients may choose less crowded EDs, reducing waiting times and overcrowding; emergency services can better coordinate transfers, alleviating pressure on high-volume centres.
Beyond the project’s official end, eCREAM aims to expand its network and include new patient groups. Most importantly, it will provide sustainable tools for long-term emergency medicine research by enabling automatic extraction of EHR data through its tool and developing a new EHR. These innovations will overcome long-standing barriers in the field, ultimately improving both research capacity and the quality of emergency care.
