Trends and Perceptions of Electronic Health Record Usage among Plastic Surgeons
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Electronic health records (EHRs) should help physicians stay organized, improve patient safety, and facilitate communication with both patients and fellow healthcare providers. However, few studies have directly evaluated physician satisfaction with EHR and its perceived impact on patient care. This study assessed trends and perceptions of EHR within the American plastic surgery community. Methods: An Institutional Review Board–approved survey that assessed demographics, patterns of EHR use, and attitudes toward EHR was deployed by the American Society of Plastic Surgeons Member Survey Research Services. Statistical analyses were performed using Stata 14.2 and QDA Miner Lite software (Version 2.0; Provalis, Montreal, Canada). Significance level was P < 0.05. Results: Among plastic surgeons who use EHR, EPIC Systems software (Epic, Verona, Wisc.) was the most common vendor, with users noting a net positive effect on the quality of care they provided to patients. Younger age and less years of experience were correlated with a more positive attitude toward EHR. Positive attitude was closely linked to shared responsibility among support staff over data entry, whereas negative attitude was tightly tied to the perceived time wasted because of EHR, followed by poor technical support and design. Conclusions: EHR use among plastic surgeons was more common in academic-associated specialties and larger practice groups. Overall, age and practice type had weak associations with perceptions of EHR usage. On average, there were slightly more positive perceptions of EHR usage than negative. The most commonly perceived issues with EHR were wasted time and barriers to user-friendliness. These findings suggest the need for greater physician involvement in EHR optimization.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it