EDUCATIONAL INTERVENTIONS TO REDUCE PAEDIATRIC PRESCRIBING ERRORS
Bibliographic record
Abstract
Background Although prescribing errors affect all age groups, they particularly affect paediatric patients, due to the challenges involved in the prescribing process. The complexity of the dose calculations, the variation of doses according to weight and the higher usage of unlicensed and off-label medications are all factors which may increase the number of prescribing errors. Education is an important intervention which may reduce the risk. Objective To review the literature to identify educational interventions which have been used to try to reduce prescribing errors in neonatal and paediatric patients. Method Systematic search of: International Pharmaceutical Abstracts (1970 to April 2014), Medline (1946 to April 2014), Embase (1974 to April 2014), PubMed (1970 to April 2014) and Cochrane (1970 to April 2014). All types of original research studies reporting educational interventions aimed at reducing prescribing errors in neonatal and paediatric patients were selected. The search included all languages. Studies were categorised according to the number and type of interventions used. The quality of the studies was assessed using the medical education research study quality instrument (MERSQI). Results Nineteen studies met the inclusion criteria. The majority had a before-after design. These trials were classified to studies which used single-educational (8), multi-educational (5) and multi-educational and non-educational interventions (6 studies). They utilised various educational strategies (e.g. tutorial, e-learning courses and posters) which aimed to improve prescribing practices and reduce prescribing errors. Seven studies were conducted in the United Kingdom, four in Spain, three in the United States, two in Australia and the remaining three studies in Canada, Argentina and Egypt. There was only one multi-centre study while the others were single-centre. Various methods were used to assess the effectiveness of these interventions including chart review, incident reports and prescribing competency assessment. 16 out of the 19 studies demonstrated that the educational interventions were effective in reducing paediatric prescribing errors. There were eight studies in which the doctors were assessed directly after the interventions while the assessment in other studies took place between 2 weeks to 4 years after implementation of the strategies. The results showed that educational interventions can have both long and short-term effects on reduction of prescribing errors. The three studies which showed insignificant results used single short (less than one hour) educational interventions. Conclusion There are only a few studies that assess educational interventions used to improve the prescribing process in paediatric wards. However, the studies did demonstrate that training and educating physicians about Good Prescribing Practice, and increasing their awareness about prescribing errors, can reduce such errors in neonatal and paediatric patients.
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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 | 0.006 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".