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

Kangli Qiu^1,2,3,4, Tianshu Zeng^1,2,3,4, Wenfang Xia^1,2,3,4

¹Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.

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Summary

This study introduces a "medicine + X" interdisciplinary simulation system for medical education, using public datasets for clinical case analysis. The data-case-simulation model enhances cross-disciplinary training and data-driven medical practice.

Keywords:

Case-driven Dataset Interdisciplinary Medicine + X Practice teaching Simulation

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Area of Science:

Medical Education
Interdisciplinary Studies
Data Science in Healthcare

Background:

Medical education increasingly requires professionals skilled in multiple disciplines.
There is a need for innovative training methods integrating clinical data analysis with diverse medical fields.

Purpose of the Study:

To develop clinical data analysis cases using public datasets for a "medicine + X" interdisciplinary simulation system.
To investigate the effectiveness of this system in enhancing cross-disciplinary medical training.

Main Methods:

Designed a "medicine + X" simulation practice system with data, case, and simulation dimensions.
Implemented a modular design with stratified models for data, analysis, and presentation.
Conducted a closed-loop practice from data processing to front-end interaction, evaluated using a modified System Usability Scale.

Main Results:

Successfully applied the system to five disease cases: gout, gastritis, cirrhosis, inflammatory bowel disease, and chronic obstructive pulmonary disease.
Demonstrated mastery of data analysis processes across model algorithms, data analysis, and result display using diverse datasets.

Conclusions:

The "Data-case-simulation" model effectively uses open-source data for clinical case analysis and statistical thinking.
The system validates efficient case simulation analysis in interdisciplinary settings, offering a data-driven paradigm for medical education.
This approach provides valuable insights for advancing "medicine + X" integration in medical training.