Trends and Perceptions of Electronic Health Record Usage among Plastic Surgeons
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.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".