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

Area of Science:

• Medical Informatics
• Human-Computer Interaction
• Clinical Decision Support

Background:

• Growing electronic health record (EHR) data volume and complexity increase clinician cognitive burden.
• Poor EHR usability and unfiltered data lead to inefficiencies, errors, and burnout, especially in emergency care.
• Patient-centered dashboards offer a solution by automating data extraction and visualization.

Purpose of the Study:

• To map literature on automated extraction and visualization of patient-centered EHR data for emergency settings.
• To investigate data selection/filtering, dashboard design strategies, and essential information for acute care overview displays.

Main Methods:

• Scoping review following the PRISMA-ScR framework.
• Comprehensive literature search in PubMed, Scopus, and IEEE Xplore (2010 onwards).
• Inclusion criteria: studies on automated data filtering, visualization, or dashboard design using EHR data.

Main Results:

• Diverse data filtering approaches used, from rule-based to AI models.
• Visualizations are designed to align with clinician cognitive workflows.
• Key data parameters include medications, allergies, vital signs, history, and directives; user-centered design is common.
• Studies report improved decision-making efficiency, treatment, and reduced cognitive load.

Conclusions:

• Automated, patient-centered dashboards enhance EHR usability for safer, faster acute care decisions.
• Further research is needed on clinical impact, interoperability, and core data elements.