Simulation-based Medical Education (SBME) has been developed as a
cost-effective means of enhancing the diagnostic skills of novice physicians
and interns, thereby mitigating the need for resource-intensive
mentor-apprentice training. However, feedback provided in most SBME is often
directed towards improving the operational proficiency of learners, rather than
providing summative medical diagnoses that result from experience and time.
Additionally, the multimodal nature of medical data during diagnosis poses
significant challenges for interns and novice physicians, including the
tendency to overlook or over-rely on data from certain modalities, and
difficulties in comprehending potential associations between modalities. To
address these challenges, we present DiagnosisAssistant, a visual analytics
system that leverages historical medical records as a proxy for multimodal
modeling and visualization to enhance the learning experience of interns and
novice physicians. The system employs elaborately designed visualizations to
explore different modality data, offer diagnostic interpretive hints based on
the constructed model, and enable comparative analyses of specific patients.
Our approach is validated through two case studies and expert interviews,
demonstrating its effectiveness in enhancing medical training.Comment: Accepted by IEEE VIS 202