100 Effect of a Brief Educational Intervention on Prescription Writing by Pediatric Residents and Hospitalists
Bibliographic record
Abstract
To measure the effect of a brief educational intervention on medication ordering by pediatric residents and hospitalists. Currently, at the Alberta Children's Hospital (ACH) medication orders are handwritten in each patient's chart. Such orders are commonly incomplete, unclear, and contain abbreviations with multiple possible meanings. Incorrect prescription writing is a significant source of medication error and a threat to patient safety (Bates, 1999; Kaushal et al., 2001). Medication ordering by pediatric residents and hospitalists was investigated over several weeks before and after a seminar on prescribing errors (Powerpoint® presentation, 30 minutes in length). A sequential chart review (n=21) measured frequency of ordering errors: defined as an order which was illegible, incomplete, contained unclear abbreviations, or whose dosage was calculated incorrectly (from mg/kg or mg/m2). Prescribing drugs to which the patient had a known allergy was also counted as an ordering error. As drug-drug interactions were not readily apparent on the order sheet, prescribing combinations of drugs with known interactions was not considered. Orders were considered individually according to guidelines published by the Institute for Safe Medication Practices (ISMP). Error frequency (total errors/total orders) decreased 27.5% (64.8% to 37.3%) following the presentation. The two phases had slight differences in: the number of orders reviewed (444 versus 314), orders per chart (22.2 versus 17.6), and standard deviation in error frequency (19.5% versus 22.3%). Though the number of orders per chart varied, influenced by length of hospitalization as well as number of medications prescribed, it should not have influenced error frequency. Overall, the two samples were similar enough to allow comparison and the difference in error frequency was found to be statistically significant using the t-test (p<0.01). Error frequency decreased from the first review to the second (64.9% to 37.3%) and was statistically significant (p<0.01). Currently, evidence suggests a brief educational intervention (t=30min) can alter prescribing behaviour and lower error frequency for orders written by hospitalists and residents. Whether this will reduce medication errors is unknown, though it is reasonable to infer.
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 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".