Medication Prescribing Errors on a Surgery Service – Addressing the Gap with a Curriculum for Surgery Residents: A Prospective Observational Study
Bibliographic record
Abstract
Abstract Background Medication prescribing errors are a source of morbidity and mortality on surgical wards, however educational interventions with proven effectiveness to reduce these errors are lacking. Our objective was to design, implement, and assess the effectiveness of a curriculum designed to reduce medication prescribing errors on a surgery service at an academic hospital without electronic order entry. Methods This was a prospective observational cohort study at a Canadian academic hospital. A medication prescribing curriculum for surgery residents was developed and implemented in July 2019. All general surgery residents (n = 16) at our institution were eligible; 13 (81%) participated. Medication prescribing errors were tracked pre-curriculum implementation (July 1, 2018-June 30, 2019) and post-curriculum (July 1-December 31, 2019). Medication prescribing errors were classified as prescription-writing (PW) or decision-making (DM). Results There were 87.5 (14.6) total medication prescribing errors per month in the pre-implementation period with 51.3 (11.9) PW and 36.3 (6.0) DM errors. Post-implementation, there were 78.7 (10.3) total errors monthly with 43.3 (9.5) PW and 35.3 (4.2) DM errors. There were significantly fewer total errors monthly in the first quarter (July–September) of the academic year post-curriculum implementation versus pre-implementation (77.7(12.7) vs. 107.3(8.1); p = 0.035) with significantly fewer PW errors monthly (40.7(13.2) vs. 68.7(9.3); p = 0.046) and no difference in DM errors monthly (37.0(2.0) vs. 38.7(5.7); p = 0.671). Conclusions Medication prescribing errors on a surgical service occurred both from prescription-writing and decision-making. Educational interventions, such as our medication prescribing curriculum, can decrease errors related to prescription writing, however the effect appears diminish over time.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".