The peer-to-peer comparison means center-to-center comparison, which requires adjusting for possible differences among centers to be fair and convincing. The first step to reach this goal is to develop a predictive model that accurately estimates each patient's probability of being admitted, starting from clinical conditions and boundary variables. Such a model would make it possible to calculate, for each ED, the expected hospitalization rate; that is, the hospitalization rate that would have been observed if the ED had behaved like the average of the EDs that provided the data to build the model itself. Comparing the observed hospitalization rate in the single ED with the expected rate derived from the model provides a rigorous method of comparing the department with the average performance, taking into account the characteristics of the patients treated and the conditions under which the ED operated. In other words, the predictive model represents the benchmark against which each ED is evaluated.

Inclusion Criteria:

• Adult
• Arrived at emergency department between 1 January 2021 and 31 December 2023

Exclusion Criteria:

• None

All adult patients who arrived at participating emergency departments between January 1, 2021 and December 31, 2023

eCREAM-UCI1 will join the MIP (Medical Informatics Platform), developed in the Human Brain Project (HBP), to federate clinical data. The MIP is an open-source, GDPR-compliant, privacy-aware platform, enabling remote and federated analyses from datasets physically distributed in various hospitals, research centers, and biobanks, without moving the data outside their original storage site to a central repository. Patient identification data will be deleted from the databases before these are uploaded to the MIP platform, and no pseudo-identifier will be available.

See https://mip.ebrains.eu/ for more details on the MIP.