Medillustrator: Improving Retrospective Learning in Physicians' Continuous Medical Education via Multimodal Diagnostic Data Alignment and Representation

TL;DR

Medillustrator is a visual analytics system designed to enhance physicians' retrospective learning in CME by aligning and representing multimodal diagnostic data at a semantic level, supporting better analysis and collaboration.

Contribution

This paper introduces Medillustrator, a novel system that improves retrospective learning for physicians through multimodal data alignment and visualization, addressing key challenges faced by novices.

Findings

Medillustrator improves data exploration and case analysis for physicians.

The system enhances retrospective learning effectiveness in CME.

User studies show increased engagement and understanding with Medillustrator.

Abstract

Continuous Medical Education (CME) plays a vital role in physicians' ongoing professional development. Beyond immediate diagnoses, physicians utilize multimodal diagnostic data for retrospective learning, engaging in self-directed analysis and collaborative discussions with peers. However, learning from such data effectively poses challenges for novice physicians, including screening and identifying valuable research cases, achieving fine-grained alignment and representation of multimodal data at the semantic level, and conducting comprehensive contextual analysis aided by reference data. To tackle these challenges, we introduce Medillustrator, a visual analytics system crafted to facilitate novice physicians' retrospective learning. Our structured approach enables novice physicians to explore and review research cases at an overview level and analyze specific cases with consistent alignment of multimodal and reference data. Furthermore, physicians can record and review analyzed results to facilitate further retrospection. The efficacy of Medillustrator in enhancing physicians' retrospective learning processes is demonstrated through a comprehensive case study and a controlled in-lab between-subject user study.