Interdisciplinary medical education practices: building a case-driven interdisciplinary simulation system based on public datasets
Abstract Background 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 establish...
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| Format: | Article |
| Language: | English |
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BMC
2025-07-01
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| Series: | BMC Medical Education |
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| Online Access: | https://doi.org/10.1186/s12909-025-07631-8 |
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| author | Kangli Qiu Tianshu Zeng Wenfang Xia Miaomiao Peng Wen Kong |
| author_facet | Kangli Qiu Tianshu Zeng Wenfang Xia Miaomiao Peng Wen Kong |
| author_sort | Kangli Qiu |
| collection | DOAJ |
| description | Abstract Background 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. Methods 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. Results 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. Conclusion 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”. |
| format | Article |
| id | doaj-art-7760438be5014e8b89720cd4e83f669d |
| institution | DOAJ |
| issn | 1472-6920 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Education |
| spelling | doaj-art-7760438be5014e8b89720cd4e83f669d2025-08-20T03:05:04ZengBMCBMC Medical Education1472-69202025-07-0125111210.1186/s12909-025-07631-8Interdisciplinary medical education practices: building a case-driven interdisciplinary simulation system based on public datasetsKangli Qiu0Tianshu Zeng1Wenfang Xia2Miaomiao Peng3Wen Kong4Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and TechnologyDepartment of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and TechnologyDepartment of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and TechnologyDepartment of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and TechnologyDepartment of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and TechnologyAbstract Background 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. Methods 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. Results 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. Conclusion 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”.https://doi.org/10.1186/s12909-025-07631-8Practice teachingDatasetCase-drivenSimulationInterdisciplinaryMedicine + X |
| spellingShingle | Kangli Qiu Tianshu Zeng Wenfang Xia Miaomiao Peng Wen Kong Interdisciplinary medical education practices: building a case-driven interdisciplinary simulation system based on public datasets BMC Medical Education Practice teaching Dataset Case-driven Simulation Interdisciplinary Medicine + X |
| title | Interdisciplinary medical education practices: building a case-driven interdisciplinary simulation system based on public datasets |
| title_full | Interdisciplinary medical education practices: building a case-driven interdisciplinary simulation system based on public datasets |
| title_fullStr | Interdisciplinary medical education practices: building a case-driven interdisciplinary simulation system based on public datasets |
| title_full_unstemmed | Interdisciplinary medical education practices: building a case-driven interdisciplinary simulation system based on public datasets |
| title_short | Interdisciplinary medical education practices: building a case-driven interdisciplinary simulation system based on public datasets |
| title_sort | interdisciplinary medical education practices building a case driven interdisciplinary simulation system based on public datasets |
| topic | Practice teaching Dataset Case-driven Simulation Interdisciplinary Medicine + X |
| url | https://doi.org/10.1186/s12909-025-07631-8 |
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