STUDY PROTOCOL article
Front. Disaster Emerg. Med.
Sec. Emergency Health Services
Volume 3 - 2025 | doi: 10.3389/femer.2025.1558444
This article is part of the Research TopicElectronic Health Records in Emergency Medicine: From Accountability to OpportunityView all 6 articles
Exploiting EHRs using natural language processing to enable research in emergency medicine: a protocol for a study on hospitalization rates
Provisionally accepted- 1Università Statale di Milano, Milan, Lombardy, Italy
- 2Ospedale San Giovanni Bosco, Turin, Piedmont, Italy
- 3Mario Negri Institute for Pharmacological Research (IRCCS), Milano, Italy
- 4Jagiellonian University, Kraków, Lesser Poland, Poland
- 5Nemocnica AGEL Košice-Šaca, Kosice-Saca, Slovakia
- 6Ashford and St Peter's Hospitals NHS Foundation Trust, Chertsey, United Kingdom
- 7University of Pavol Jozef Šafárik, Košice, Slovakia
- 8University of Crete School of Medicine and 7th Health Region of Crete, Heraklion, Greece
- 9Ospedale Santa Maria delle Grazie, Naples, Campania, Italy
- 10Univerzitetni Kliniĉni Center, Maribor, Slovenia
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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 difficulities 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, theis 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.
Keywords: Electronic Health Records, Hospital emergency service, Hospitalization, Patient Discharge, Quality of Health Care, Statistical model
Received: 10 Jan 2025; Accepted: 31 Jul 2025.
Copyright: © 2025 Rubini, Aprà, Ghilardi, Górka, Hricova, John, Lazúrová, Mitro, Nattino, Notas, Pandolfini, Porta, Prosen, Sharma, Strnad and Bertolini. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Chiara Pandolfini, Mario Negri Institute for Pharmacological Research (IRCCS), Milano, Italy
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