Interdisciplinary medical education practices: building a case-driven interdisciplinary simulation system based on...

Recent advancements in medical education underscore the importance of training professionals who are proficient in multiple disciplines. This study aims to develop clinical data analysis cases centered around diseases by utilizing public datasets, and to investigate the establishment of a "medicine + X" simulation practice system within the framework of interdisciplinary disciplines.

From a multi-disciplinary perspective, we designed a cross-disciplinary "medicine + X" subject simulation practice system based on three dimensions: data, case, and simulation. This system comprises three parts: dataset classification, dataset modeling, and dataset clinical analysis. The entire interdisciplinary simulation system adheres to the concept of functional modular design and employs a model stratification method to achieve the division of data, analysis, and presentation models. This creates a closed-loop practice that spans data sample selection and processing to front-end interaction. Finally, we used a modified version of the System Usability Scale (SUS) questionnaire to evaluate the interdisciplinary simulation system.

Five cases of gout, gastritis, cirrhosis, inflammatory bowel disease, and chronic obstructive pulmonary disease were utilized to master the standard process of data analysis across various datasets from multiple dimensions of the model algorithm, data analysis, and result display.

The "Data-case-simulation" trinity practice teaching model enables students to utilize open-source datasets for case analysis, employing clinical index modeling and statistical thinking. This verifies the efficiency of case simulation analysis within interdisciplinary scenarios and provides a data-driven practice paradigm for medical education innovation. This model holds significant reference value for promoting in-depth cross-disciplinary integration of "medicine + X".