Medication errors room: a simulation to assess the medical, nursing and pharmacy staffs' ability to identify errors related to the medication‐use system
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: The medication-use system in hospitals is very complex. To improve the health professionals' awareness of the risks of errors related to the medication-use system, a simulation of medication errors was created. The main objective was to assess the medical, nursing and pharmacy staffs' ability to identify errors related to the medication-use system using a simulation. The secondary objective was to assess their level of satisfaction. METHOD: This descriptive cross-sectional study was conducted in a 500-bed mother-and-child university hospital. A multidisciplinary group set up 30 situations and replicated a patient room and a care unit pharmacy. All hospital staff, including nurses, physicians, pharmacists and pharmacy technicians, was invited. Participants had to detect if a situation contained an error and fill out a response grid. They also answered a satisfaction survey. RESULTS: The simulation was held during 100 hours. A total of 230 professionals visited the simulation, 207 handed in a response grid and 136 answered the satisfaction survey. The participants' overall rate of correct answers was 67.5% ± 13.3% (4073/6036). Among the least detected errors were situations involving a Y-site infusion incompatibility, an oral syringe preparation and the patient's identification. Participants mainly considered the simulation as effective in identifying incorrect practices (132/136, 97.8%) and relevant to their practice (129/136, 95.6%). Most of them (114/136; 84.4%) intended to change their practices in view of their exposure to the simulation. CONCLUSIONS: We implemented a realistic medication-use system errors simulation in a mother-child hospital, with a wide audience. This simulation was an effective, relevant and innovative tool to raise the health care professionals' awareness of critical processes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.089 | 0.340 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".