Use of Electronic Medical Records by Physicians and Students in Academic Internal Medicine Settings
Bibliographic record
Abstract
PURPOSE: Electronic medical records (EMRs) have been touted as one method to improve quality and safety in medical care, and their use has recently increased. The purpose of this study is to describe current use of EMRs by medical students at U.S. and Canadian medical schools. METHOD: In 2006 the authors performed a cross-sectional survey of the Clerkship Directors in Internal Medicine institutional members at U.S. and Canadian academic health centers. Outcome measures included implementation of EHRs, EHR use by students, and the challenges of having students use EMRs. RESULTS: Of 110 members, 82 (74.5%) responded. Of those 82, 48 (58%) reported using an EMR in the ambulatory setting (excluding Veterans' Affairs medical centers) of their institutions, and only 21 of those 48 (44%) had policies regarding medical student documentation of progress notes in the EMR during the ambulatory internal medicine (IM) clerkship. Schools were dichotomously split; about half (23/48, 48%) required and about half (25/48, 52%) prohibited allowing students to document in the EMR. The programs that prohibited medical students from documenting in the EMR primarily cited billing concerns. Other issues regarding student use of EMRs included student access, faculty concerns, and note quality. CONCLUSIONS: Use of EMRs by IM clerkship students is common, yet many institutions do not have policies regarding student use. Where policies do exist, they vary, and many prohibit students from using EMRs. Concerns about documentation as it relates to billing seem to be a significant factor in prohibiting students' use of EMRs.
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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".