Evaluation of a medication order writing standards policy in a regional health authority
Bibliographic record
Abstract
BACKGROUND: The Winnipeg Regional Health Authority (WRHA) implemented a medication order writing standards (MOWS) policy (including banned abbreviations) to improve patient safety. Widespread educational campaigns and direct prescriber feedback were implemented. METHODS: We audited orders within the WRHA from 2005 to 2009 and surveyed all WRHA staff in 2011 about the policy and suggestions for improving education and compliance. RESULTS: Overall, orders containing banned abbreviations, acronyms or symbols numbered 2261/8565 (26.4%) preimplementation. After WRHA-wide didactic education, the proportion declined to 1358/5461 (24.9%) (p = 0.043) and then, with targeted prescriber feedback, to 1186/6198 (19.1%) (p < 0.0001). A survey of 723 employees showed frequent violations of the MOWS, despite widespread knowledge of the policy. Respondents supported ongoing efforts to enforce the policy within the WRHA. Nonprescribers were significantly more likely than prescribers to agree with statements regarding enhancing compliance by defining prescriber/transcriber responsibilities and placing sanctions on noncompliant prescribers. DISCUSSION: Education, raising general awareness and targeted feedback to prescribers alone are insufficient to ensure compliance with MOWS policies. WRHA staff supported ongoing communication, improved tools such as compliant preprinted orders and reporting and feedback about medication incidents. A surprising number of respondents supported placing sanctions on noncompliant prescribers. CONCLUSION: Serial audits and targeted interventions such as direct prescriber feedback improve prescription quality in inpatient hospital settings. Education plus direct prescriber feedback had a greater impact than education alone on improving compliance with a MOWS policy. Future efforts at the WRHA to improve compliance will require an expanded focus on incentives, resources and development of action plans that involve all affected staff, not just prescribers. Plans include continued advertising, MOWS summaries in all charts, all-staff education, reminders and exploration of sustainable interventions for targeted feedback for prescribers.
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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 | 0.121 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".