Do High‐Risk Medicines Alerts Influence Practice?
Bibliographic record
Abstract
ABSTRACT Background Medicine‐related adverse events are prevalent, costly and mostly preventable. The High Risk Medicines Working Party (Victoria) developed and distributed three high‐risk medicines alerts – wrong route of administration of oral medicines, subcutaneous insulin and unfractionated heparin – and accompanying audit tools in 2008 and 2009. Aims To determine the impact of the three high‐risk medicines alerts on Victorian health services; to assess the clinical relevance and utility of the audit tools; to identify barriers to implementing recommendations; and to obtain feedback and suggestions for future alert topics. Method A cross‐sectional survey was undertaken from 6 to 31 July 2009 using an online questionnaire. The questionnaire was distributed to 90 metropolitan, regional and rural public health services in Victoria and approximately 200 members of the Quality Use of Medicines Network (Victoria). Results Most of the 90 respondents were pharmacists (53%) and nurses (31%). 53 (59%) respondents reported making changes as a result of receiving the high‐risk medicines alerts – 21 (40%) concerned the wrong route of administration, 12 (23%) subcutaneous insulin and 7 (13%) unfractionated heparin. Barriers to implementation included time constraints, inadequate staff and resources, excessive paperwork and competing priorities. A minority of respondents indicated some alerts were not relevant to small rural services. Suggestions for improving the audit tools included making them less labour intensive, enabling electronic responses and ensuring their distribution is coordinated with other medicine‐related tools. Conclusion High‐risk medicines alerts and the accompanying audit tools facilitated change but there were some barriers to their implementation, such as time and resource constraints. Not all alerts and audit tools were relevant to all health services.
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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 | 0.020 | 0.180 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".