How Does Learnability of Primary Care Resident Physicians Increase After Seven Months of Using an Electronic Health Record? A Longitudinal Study
Bibliographic record
Abstract
BACKGROUND: Electronic health records (EHRs) with poor usability present steep learning curves for new resident physicians, who are already overwhelmed in learning a new specialty. This may lead to error-prone use of EHRs in medical practice by new resident physicians. OBJECTIVE: The study goal was to determine learnability gaps between expert and novice primary care resident physician groups by comparing performance measures when using EHRs. METHODS: We compared performance measures after two rounds of learnability tests (November 12, 2013 to December 19, 2013; February 12, 2014 to April 22, 2014). In Rounds 1 and 2, 10 novice and 6 expert physicians, and 8 novice and 4 expert physicians participated, respectively. Laboratory-based learnability tests using video analyses were conducted to analyze learnability gaps between novice and expert physicians. Physicians completed 19 tasks, using a think-aloud strategy, based on an artificial but typical patient visit note. We used quantitative performance measures (percent task success, time-on-task, mouse activities), a system usability scale (SUS), and qualitative narrative feedback during the participant debriefing session. RESULTS: There was a 6-percentage-point increase in novice physicians' task success rate (Round 1: 92%, 95% CI 87-99; Round 2: 98%, 95% CI 95-100) and a 7-percentage-point increase in expert physicians' task success rate (Round 1: 90%, 95% CI 83-97; Round 2: 97%, 95% CI 93-100); a 10% decrease in novice physicians' time-on-task (Round 1: 44s, 95% CI 32-62; Round 2: 40s, 95% CI 27-59) and 21% decrease in expert physicians' time-on-task (Round 1: 39s, 95% CI 29-51; Round 2: 31s, 95% CI 22-42); a 20% decrease in novice physicians mouse clicks (Round 1: 8 clicks, 95% CI 6-13; Round 2: 7 clicks, 95% CI 4-12) and 39% decrease in expert physicians' mouse clicks (Round 1: 8 clicks, 95% CI 5-11; Round 2: 3 clicks, 95% CI 1-10); a 14% increase in novice mouse movements (Round 1: 9247 pixels, 95% CI 6404-13,353; Round 2: 7991 pixels, 95% CI 5350-11,936) and 14% decrease in expert physicians' mouse movements (Round 1: 7325 pixels, 95% CI 5237-10,247; Round 2: 6329 pixels, 95% CI 4299-9317). The SUS measure of overall usability demonstrated only minimal change in the novice group (Round 1: 69, high marginal; Round 2: 68, high marginal) and no change in the expert group (74; high marginal for both rounds). CONCLUSIONS: This study found differences in novice and expert physicians' performance, demonstrating that physicians' proficiency increased with EHR experience. Our study may serve as a guideline to improve current EHR training programs. Future directions include identifying usability issues faced by physicians when using EHRs, through a more granular task analysis to recognize subtle usability issues that would otherwise be overlooked.
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 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 | 0.000 |
| 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) | 0.000 | 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".