ArticleBMC medical education2025
Interdisciplinary medical education practices: building a case-driven interdisciplinary simulation system based on public datasets.
Article in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Outcome-based education and student learning in probability and statistics: the mediating roles of engagement and self-efficacy in a new liberal arts context.Frontiers in psychology · 2026Article
- Institutionalizing convergence education for medical artificial intelligence.Biomedical engineering letters · 2025Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundRecent 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.
methodsFrom 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.
resultsFive 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.
conclusionThe "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".
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.