A modular visual analytics dashboard for patient health data

In everyday outpatient care, office-based medical professionals often face a growing volume of patient records, spanning years or even decades, while consultation times remain critically short. Much of this information is embedded in unstructured documents such as physician letters, lab reports, and diagnostic notes. The lack of time makes it difficult for physicians to comprehend the patients’ medical history and to base decisions on the full health and disease history. This paper presents a modular Visual Analytics approach particularly designed to support general practitioners in navigating long-term patient histories. Using transformer-based large language models, the system automatically extracts and classifies relevant clinical entities from unstructured documents. These extracted elements are mapped to a set of predefined data categories and visualized through tailored, interactive components, such as medication intake timelines, blood count diagrams, and named-entity highlighted texts. Visual components are selected dynamically based on content type and clinical relevance, allowing medical professionals to explore patient histories efficiently and to identify critical information at a glance. The modular architecture facilitates integrating new document types and visual modules, ensuring flexibility and scalability. Designed for real-world applicability, the system aims to reduce cognitive load, improve information accessibility, and support time-sensitive decision-making in primary care.