MedsOnCall Pager App: A Pilot Project for Practicing Safe Clinical Decision-making
Bibliographic record
Abstract
Errors in clinical decision-making contribute to approximately half of in-hospital adverse events. The steep learning curve when students transition to residents is particularly susceptible to increased errors. Decision-making skills are a major contributor to preparedness for residency and educators agree that decision-making should be purposefully taught and tested. Despite this, little structured assessment of decision-making currently exists. This innovation report describes the development and piloting of the MedsOnCall (MOC) Pager App, a simulated pager program designed as a learning and assessment tool for senior medical students and junior residents to practice safe clinical decision-making as they transition between these two roles. Learners are randomly "paged" by the app about a list of virtual patients. To answer, they must integrate pertinent patient information efficiently. Learners then receive a page-management question that further probes their decision-making skills by asking them to consider the urgency and their level of confidence when determining the virtual patient's needs. The pilot version of the app was successfully alpha-tested in 2016 and 2017 with twenty fourth year medical students at our institution. Subjectively, students greatly enjoyed using the MOC Pager app to practice answering pages in a safe environment. The app was then adapted for the National Cardiac Surgery Bootcamp in 2017 for use by first-year residents. With demonstrated success as a pilot project, our group aims to rebuild the app for customizable use by multidisciplinary learners anywhere in the world simultaneously. We also plan to collect validity evidence, integrate in-app feedback capability, and disseminate the app on multiple platforms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".