Exploiting EHRs using natural language processing to enable research in emergency medicine: a protocol for a study on hospitalization rates - Rubini, Vicky; Aprà, Franco; Ghilardi, Giulia Irene; Górka, Jacek; Hricova, Katarina; John, Isaac; Lazúrová, Zora; Mitro, Peter; Nattino, Giovanni; Notas, George; Pandolfini, Chiara; Porta, Giovanni; et al. (Frontiers in Disaster and Emergency Medicine, 2025)
Increasing demands on emergency departments (EDs) call for optimized decision-making processes to improve patient outcomes and resource allocation. Overcrowding is a significant issue, and the propensity of EDs to hospitalize patients is a key contributing factor to limiting in-patient bed availability, with inappropriate decisions negatively impacting healthcare quality and costs. In this setting research in emergency medicine to improve these difficulties is challenging. The main obstacles are the large volume of cases handled, the paucity of staff availability, and the resulting lack of time to dedicate to data entry. Furthermore, the electronic health record (EHR) systems currently used in EDs are not optimized for collection of data for research. Even retrospective data analyses cannot be performed due to the lack of robust data. Moreover, the EHR contains not only structured data but also abundant information in a free-text format which is challenging to use for research purposes. This protocol describes a study, the Use Case 1 study, which is part of the more general Horizon Europe eCREAM (enabling Clinical Research in Emergency and Acute-care Medicine) project. The study will test the reliability of an advanced natural language processing model set up in eCREAM to exploit EHRs by extracting robust, structured data to enable research in EDs. Specifically, the study will test the validity of the data extracted from the EHRs by addressing the issue of hospitalization rate. We will develop a predictive model to assess emergency department hospitalization rates, thereby enabling standardized comparisons across centers, ultimately leading to improved decision-making and reduced unnecessary hospital admissions.Retrospective patient data from 2021 to 2023 from 30 centers across Europe will be analyzed, and multivariable models will be employed to predict hospitalization and adjust comparisons between centers. The results are expected to improve decision-making in these departments. More generally, should the data extraction system prove valid, our results would serve as a practical demonstration that, despite the abundance of free-text data, EHRs can be exploited to conduct research in the emergency medicine field.Clinical trial registrationClinicaltrials.gov, identifier: NCT06354764.