Editorial: Electronic health records in emergency medicine: from accountability to opportunity

The adoption of Electronic Health Records (EHRs) has fundamentally transformed modern medicine. However, nowhere is the impact of EHR more challenging than in the field of Emergency Medicine (EM). EM is characterized by intense life-or-death decision-making under time constraints, often with limited resources and limited time to record critical information. While EHRs offer a wealth of clinical data, the transition from simple digital documentation to actionable, data-driven research and improvements in clinical outcomes remains a complex process. This Special Issue brings together 11 innovative contributions that explore the “evolutionary pipeline” of EHRs: from building secure, cross-border infrastructures and navigating complex legal landscapes to deploying real-time clinical tools that improve patient outcomes.

Setting the foundations for patient data safety: federated systems and addressing data scarcity

The first stage for the development of EM EHR pipelines involves creating systems that enable large-scale research without compromising patient data confidentiality. The eCREAM (enabling Clinical Research in Emergency and Acute Care Medicine) project serves as a cornerstone for this discussion. Bertolini et al. outline the necessity of standardized EHR formats across Europe to enable interoperability, arguing that the EHR must evolve from a clinical log into a research-ready asset.

A major obstacle for this evolution is overcoming the “privacy-exploitation barrier.” Melissargos et al. demonstrate how the Medical Informatics Platform (MIP) can facilitate federated analytics. By allowing “the code to visit the data” while records remain behind local firewalls, this architecture facilitates multi-centric research across national borders. Other studies point in the same direction. Federated learning enables hospitals to manage shared AI models without sending raw patient data outside their systems, helping them comply with GDPR and HIPAA (1, 2). Models built this way can perform almost as well as those trained on pooled data, and additional privacy tools can be added to keep the shared updates safe (3).

Similarly, Muñoyerro-Muñiz et al. present the Andalusian PAGEM platform as a scalable model for regional evidence generation, utilizing a medical data lake of over 15 million patients. Magnini et al. address the challenges of data scarcity in non-English speaking regions and the limitations they create for quality data analytics. They provide evidence on how Large Language Models (LLMs) can automate the translation and annotation of clinical datasets from different languages, bridging the gap between limited data, low-resource settings, and high-quality NLP research. Still, as long as fears of data misinterpretation and “hallucinations” persist, the need for “human-in-the-loop” verification when using LLMs is of utmost importance.

Ethics, governance, and safety. How can we guard EHRs and their ethical use?

Protecting EHRs needs both well-established, usable technology and clear rules. Reviews call for strong access controls, encryption, and audit logs, along with clear consent that patients can revoke at any time (4). For research use, many authors argue that careful, low-risk access with strong oversight can be more practical than obtaining consent from every patient, an important point in emergency care, where consent is often not possible (5).

Therefore, the second stage of the pipeline addresses the societal and regulatory frameworks that should govern data use. Matei et al. highlight the critical role of a multidisciplinary legal and regulatory task force in navigating GDPR issues. Their work provides a blueprint for obtaining consent exemptions in emergency settings, where retrospective individual consent is often impossible or impractical.

Data quality and safety also require standardizing clinical terms. McCue et al. call for standardized Adverse Drug Event (ADE) values. They argue that by exploiting EHR information, we can connect specific medical products to adverse clinical outcomes, and that improved documentation strengthens clinical decision support systems and prevents medication-related injuries.

Clinical applications utilizing EHRs and leading to meaningful outcomes

The broader evidence shows that EHRs help most when used as active tools, not just as digital files. Alerts and decision support built into the EHR can lower medication errors, especially in the emergency department (6), and giving patients access to their records can improve care in long-term illnesses (7). Even so, these gains are usually small and do not happen on their own: they depend on good design and on fitting the tools into daily work (8).

Thus, the final stage of such a perceived pipeline is the translation of data into actionable clinical rules and practices. A number of papers in this Research Topic focus on optimizing ED functions and predicting patient trajectories. Porta et al. present a protocol for real-time dashboards that monitor crowding levels and system bottlenecks, empowering both administrators and citizens with situational awareness. Complementing this, Rubini et al. describe a protocol for using NLP to extract variable information from free-text notes to predict hospitalization rates, enabling performance standardization and comparisons across different EDs.

The collection also focuses on specific patient behaviors that may negatively affect ED resources. Williams et al. utilize K-means clustering to move beyond simple visit counts, providing a mathematical methodology for defining “frequent attenders” based on the temporal spacing of visits. Furthermore, Alshamsi et al. use a mixed-method approach to investigate the causes of patients “leaving against medical advice” (LAMA), identifying nationality and triage category as key predictors and emphasizing the need for targeted patient education.

Finally, the collection addresses the reliability of novel care models that can be developed in an era of digital integration. Hess et al. provide a subanalysis of the TEMS trial, demonstrating that remote tele-EMS physicians achieve diagnostic concordance comparable to on-site physicians. These findings support the safe expansion of telemedical systems as a solution to physician shortages in rural areas.

The research presented in this special issue demonstrates the impact digital EHRs can have on the field of Emergency Medicine. How this collections leads to a concrete process of EHR use is summarized in Figure 1. EHRs are no longer passive information storage systems but dynamic tools that, when integrated with appropriately secure analytics and artificial intelligence, can predict crowding, identify at-risk populations, and ensure diagnostic accuracy. However, it should be noted that the success of this technological revolution is closely tied to several societal factors. These factors must include strong ethical governance oversight tools, standardized safety and cybersecurity systems, utilization of multilingual approaches (especially in EU contexts), adequate understanding of patient behaviors, and, most importantly, investment in the improvement of the technological literacy levels of both healthcare professionals and the general public. Furthermore, these tools must focus not only on better data collection but also on the reduction of the cognitive load of overworked EM clinicians.

As we move toward the realization of the unified European Health Data Space, the methodologies described in this collection may provide a scientific pathway for more resilient, transparent, and efficient emergency medical services.

We thank the authors, editors, and reviewers for their valuable contributions to this multidisciplinary dialogue.

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Footnotes

Edited and Reviewed by: Theodore Chan, University of California, San Diego, United States