A Cardiology Handbook App to Improve Medical Education for Internal Medicine Residents: Development and Usability Study
Bibliographic record
Abstract
BACKGROUND: At most institutions, internal medicine residents struggle with balancing clinical duties and learning opportunities, particularly during busy cardiology ward rotations. To improve learning experiences for residents, we helped develop a cardiology handbook app to supplement cardiology education. OBJECTIVE: The aim of this study was to report the development, implementation, and preliminary impact of the Krannert Cardiology Handbook app on graduate medical education. METHODS: In June 2017, 122 residents at Indiana University were invited to download the digital handbook in the Krannert app. The Krannert app featured a total of 13 chapters written by cardiology fellows and faculty at Indiana University. Residents were surveyed on their self-reported improvement in cardiology knowledge and level of satisfaction after using the Krannert app. Residents were also surveyed regarding their preference for a digital handbook app versus a paper handbook. RESULTS: Of the 122 residents, 38 trainees (31.1%) participated in survey evaluations. Among all respondents, 31 app users (82%) reported that the app helped improve their cardiology knowledge base. The app had an overall favorable response. CONCLUSIONS: The Krannert app shows promise in augmenting clinical education in cardiology with mobile learning. Future work includes adding new topics, updating the content, and comparing the app to other learning modalities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".