Improving medication safety in a paediatric hospital: a mixed-methods evaluation of a newly implemented computerised provider order entry system
Bibliographic record
Abstract
OBJECTIVES: Computerised provider order entry (CPOE) systems have been implemented around the world as a solution to reduce ordering and transcription errors. However, previous literature documented many challenges to attain this goal, especially in paediatric settings. The objectives of this study were to (1) analyse the impact of a paediatric CPOE system on medication safety and (2) suggest potential error prevention strategies. METHODS: A pre-post observational study was conducted at the pilot ward (n=60 beds) of a paediatric academic health centre through mixed methods. The implementation project and medication management workflows were described through active participation to the project management team, observation, discussions and analysis of related documents. Furthermore, using incident reports, the nature of each error and error rate was compared between the preperiod and postperiod. RESULTS: The global error rate was lower, but non-statistically significant, in the post implementation phase, which was mostly driven by a significant reduction in errors during order acknowledgement, transmission and transcription. Few errors occurred at the prescription step, and most errors occurred during medication administration. Furthermore, some errors could have been prevented using a CPOE in the pre-implementation period, and the CPOE led to few technology-related errors. DISCUSSION AND CONCLUSION: This study identified both intended and unintended effects of CPOE adoption through the entire medication management workflow. This study revealed the importance of simplifying the acknowledgement, transmission and transcribing steps through the implementation of a CPOE to reduce medication errors. Improving the usability of the electronic medication administration record could help further improve medication safety.
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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 | 0.025 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".