Look-alike, sound-alike medication perioperative incidents in a regional Australian hospital: assessment using a novel medication safety culture assessment tool
Bibliographic record
Abstract
BACKGROUND: Medication safety remains a global concern, with governments and organizations striving to mitigate preventable patient harm across healthcare systems. Look-alike, sound-alike medication incidents and the safety culture are widely acknowledged as a contributor to medication errors, particularly within the high-risk perioperative environment. The Medication Safety Culture Indicator Matrix (MedSCIM) is a novel tool developed by the Canadian Institute for Safe Medication Practices to assess the maturity of the medication safety culture. This study aims to delineate look-alike sound-alike (LASA) medication incidents reported in the pharmacy and perioperative settings of an Australian hospital and assess the maturity of the medication safety culture. METHODS: The study setting is within a large regional hospital in Australia, servicing both adult and paediatric populations. Medication incidents from 1 April 2018 to 1 April 2023 were retrospectively gathered from the Clinical Incident Management System, Riskman®. Data and statistical analyses were carried out using Microsoft Excel®. The necessary approvals were secured from the Heath Service Human Research and Ethics Committee. RESULTS: During the 5-year period, a total of 246 (4.1%) of the 6002 medication incidents within the health service were identified as meeting the inclusion criteria. Of the 246 medication incidents, 63.0% were identified from the Pharmacy Department, while 22.0% and 15.0% were from the Post Anaesthetic Care Unit and Anaesthetics Department, respectively. The most frequently reported incident classification in both the Anaesthetics Department and Post Anaesthetic Care Unit was 'incorrect dose', followed by 'incorrect medication'. Throughout the 5-year period, 46 (18.7%) of the 246 medication incidents were attributed to look-alike, sound-alike sources of error, predominantly identified in the Pharmacy Department (73.9%), followed by the Anaesthetics Department (17.4%) and the Post Anaesthetic Care Unit (8.7%). High-risk medications were most frequently reported to the Anaesthetics Department. Packaging (packaging alone, naming and packaging, and syringe swaps) was determined to be a contributing factor in 30 (65.2%) of the 46 LASA medication incidents. MedSCIM assessment revealed a reactive medication safety culture. Additionally, the medication incident report documentation was found to be mostly complete or semi-complete. CONCLUSION: Our analysis delineated medication incidents occurring across the entire medication management cycle and identified incidents related to LASA medications as a contributor to medication incidents across these clinical settings. This novel medication safety culture tool assessment highlighted opportunities for improvement with clinical incident documentation.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".