Automated Filtering and Visualization of Patient-Centered Data from Electronic Health Records in Emergency Care: A Scoping Review.

Purpose: This scoping review aims to map the current literature on the automated extraction and visualization of patient-centered information from EHRs for emergency settings. It investigates 1) how clinically relevant data is selected and filtered, 2) which design strategies are used in dashboard development, and 3) what information is considered essential for overview displays in acute care and comparable contexts. Methods: The review follows the PRISMA-ScR framework. A comprehensive literature search was conducted across major databases (PubMed, Scopus, IEEE Xplore) for studies published from 2010 onwards. Studies were included if they examined automated data filtering, visualization, or dashboard design using EHR data. Results: Included studies demonstrate a range of approaches to data filtering, from rule-based systems to Artificial Intelligence-driven models. They emphasize the importance of aligning visualizations with clinicians' cognitive workflows. Relevant parameters frequently included medications, allergies, vital signs, past medical history, and care directives. Design processes often incorporated user-centered and iterative methods, though evaluation rigor varied widely. Several studies report improvements in decision-making efficiency, treatment, and cognitive load reduction. Conclusion: Automated, patient-centered dashboards can improve EHR usability and support safer, faster decision-making in acute care. However, further research is needed to evaluate clinical impact, ensure interoperability, and define core data elements across settings.