Self-Perceptions of Readiness to Use Electronic Health Records Among Medical Students: Survey Study
Bibliographic record
Abstract
BACKGROUND: Although several national organizations have declared the ability to work with electronic health records (EHRs) as a core competency of medical education, EHR education and use among medical students vary widely. Previous studies have reported EHR tasks performed by medical students, but students' self-perceived readiness and comfort with EHRs are relatively unknown. OBJECTIVE: This study aimed to better understand medical students' self-perceived readiness to use EHRs to identify potential curricular gaps and inform future training efforts based on students' perspectives. METHODS: The authors deployed a survey investigating self-perceived comfort with EHRs at 2 institutions in the United States in May 2019. Descriptive statistics were generated regarding demographics, comfort level with various EHR-related tasks, and cross-institutional comparisons. We also assessed the impact of extracurricular EHR experience on comfort level. RESULTS: In total, 147 medical students responded, of which 80 (54.4%) were female, with equal distribution across all 4 years of training. Overall confidence was generally higher for students with longer extracurricular EHR experience, even when adjusted for age, gender, year of training, and institution. Students were most comfortable with tasks related to looking up information in the EHR and felt less comfortable with tasks related to entering new information and managing medications. Fourth-year students at both schools reported similar levels of comfort with EHR use, despite differences in preclinical EHR training. Open-ended comments emphasized the value of experiential training over didactic formats. CONCLUSIONS: Information entry and medication management in the EHR represent areas for future curricular development. Experiential training via extracurricular activities and early clinical exposure may be high-yield approaches to help medical students achieve critical EHR competencies.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".