Environmental Impact and Provider Satisfaction Associated with ePrescriptions in Otolaryngology: A Quality Improvement Study
Bibliographic record
Abstract
Importance ePrescriptions are associated with increased patient satisfaction, decreased provider burden, decreased administrative costs, and a positive impact on planetary health. However, ePrescription uptake by physicians is historically low and acts as a barrier to reaping the benefits therein. Objective We aimed to attain 20% overall usage of ePrescriptions in the Department of Otolaryngology-Head and Neck Surgery (OHNS) at the London Health Sciences Centre (LHSC) by December 2023 through systematic implementation of change ideas. Design Pre-post intervention design. Setting Ambulatory clinics in OHNS at LHSC, an academic hospital. Participants Fourteen staff and 15 resident physicians in OHNS; 38 patients in pediatric otolaryngology clinic. Intervention or Exposure A root-cause analysis identified potential obstacles to ePrescribing. Change ideas, including educational seminars, surveys, quarterly reporting of ePrescription usage, and public encouragement of top ePrescribers in the department, were implemented and tested using Plan-Do-Study-Act cycles. Main Outcome Measures Percent ePrescription usage and carbon footprint savings associated with ePrescriptions were measured. Provider and patient satisfaction surveys were conducted as balancing measures to assess for the perception of increased burden on providers. Results During the pre-intervention and post-intervention phases, a total of 400 and 1000 ePrescriptions were prescribed by the department, respectively. There was a statistically-significant increase in the mean proportion of ePrescriptions used before (mean: 9.7%; sd = 7.6) and after (mean: 40.7%; sd = 6.4) the intervention ( p < 0.001), which exceeded the goal. SPC charting suggested special cause variation, signifying a statistically-significant improvement. Additionally, a reduction of 125.9 lbs of CO 2 equivalents was associated with ePrescription use. 66.7% of providers rated overall satisfaction with ePrescriptions at 7/10 or higher, and 76.9% indicated that patients either sometimes, usually, or always opted for ePrescriptions when given the choice. Conclusion and Relevant Our change ideas increased ePrescription usage in an academic OHNS department and were associated with increased planetary health savings and provider satisfaction.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".