Integrating engineering into medical education: A scoping review
Bibliographic record
Abstract
Background: As healthcare systems grow increasingly complex and technology-driven, there is a pressing need to equip future physicians with problem-solving skills, systems thinking and design principles that define engineering disciplines. Although isolated programmes have integrated engineering into medical training, there is a limited synthesis of how these approaches are operationalised and evaluated. Objective: This narrative review explores the integration of engineering principles into medical education and identifies models, thematic trends and opportunities for interdisciplinary innovation. Methods: Relevant literature was identified through a comprehensive search of PubMed, Scopus, IEEE Xplore and grey literature sources using keywords related to engineering mindsets, medical education and interdisciplinary training. A flexible and exploratory approach was used to include peer-reviewed and conceptual articles that addressed the application of systems thinking, simulation, quality improvement (QI) and design in medical training contexts. Results: Fifteen articles spanning North America and Europe were included in the study. Four themes emerged: (1) engineering-based curricula and dual-degree programmes; (2) simulation, modelling and systems thinking; (3) healthcare systems engineering and QI and (4) interdisciplinary collaboration and innovation. Programmes such as EnMED and MEDTEC exemplify dual-degree initiatives that foster innovation capacity. Simulation tools enhance experiential learning of physiology and pharmacokinetics. Lean methodology, human factors and system optimisation are embedded in QI education. Interdisciplinary programmes, such as healthcare hackathons and design thinking challenges, cultivate real-world problem-solving and team-based innovation. Conclusion: Integrating engineering principles into medical education enhances learners’ ability to navigate complex technology-intensive healthcare systems. Embracing systems thinking, process design and interdisciplinary collaboration represents curricular enhancement and a fundamental evolution in preparing future physician-innovators.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.023 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".