Technology and clinician-learner interaction: how clinicians expect introduction of a new electronic health record to affect educational practice
Bibliographic record
Abstract
Abstract Introduction Electronic health records (EHRs) are increasingly common platforms used in medical settings to capture and store patient information, but their implementation can have unintended consequences. One particular risk is damaging clinician-learner-interactions, but very little has been published about how EHR implementation affects educational practice. Given the importance of stakeholder engagement in change management, this research sought to explore how EHR implementation is anticipated to affect clinician-learner interactions, educational priorities and outcomes. Methods Semi-structured interviews were conducted with a group of practicing oncologists who work in outpatient clinics while also providing education to medical student and resident trainees. Data regarding perceived impact on the teaching dynamic between clinicians and learners were collected prior to implementation of an EHR and analyzed thematically. Results Physician educators expected EHR implementation to negatively influence their engagement in teaching and the learning they themselves normally gain through teaching interactions. Additionally, EHR implementation was expected to influence learners by changing what is taught and the students’ role in clinical care and the educational dynamic. Potential benefits included harnessing learners’ technological aptitude, modeling adaptive behaviour, and creating new ways for students to be involved in patient care. Conclusion Anticipating the concerns clinicians have about EHR implementation offers both potential to manage change to minimize disruptions caused by implementation and a foundation from which to assess actual educational impacts.
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 | 0.011 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".