The Effective Strategies to Avoid Medication Errors and Improving Reporting Systems
Bibliographic record
Abstract
BACKGROUND: Population-based studies from several countries have constantly shown excessively high rates of medication errors and avoidable deaths. An efficient medication error reporting system is the backbone of reliable practice and a measure of progress towards achieving safety. Improvement efforts and system changes of medication error reporting systems should be targeted towards reductions in the likelihood of injury to future patients. However, the aim of this review is to provide a summary of medication errors reporting culture, incidence reporting systems, creating effective reporting methods, analysis of medication error reports, and recommendations to improve medication errors reporting systems. METHODS: Electronic databases (PubMed, Ovid, EBSCOhost, EMBASE, and ProQuest) were examined from 1 January 1998 to 30 June 2020. 180 articles were found and 60 papers were ultimately included in the review. Data were mined by two reviewers and verified by two other reviewers. The search yielded 684 articles, which were then reduced to 60 after the deletion of duplicates via vetting of titles, abstracts, and full-text papers. RESULTS: Studies were principally from the United States of America and the United Kingdom. Limited studies were from Canada, Australia, New Zealand, Korea, Japan, Greece, France, Saudi Arabia, and Egypt. Detection, measurement, and analysis of medication errors require an active rather than a passive approach. Efforts are needed to encourage medication error reporting, including involving staff in opportunities for improvement and the determination of root cause(s). The National Coordinating Council for Medication Error Reporting and Prevention taxonomy is a classification system to describe and analyze the details around individual medication error events. CONCLUSION: A successful medication error reporting program should be safe for the reporter, result in constructive and useful recommendations and effective changes while being inclusive of everyone and supported with required resources. Health organizations need to adopt an effectual reporting environment for the medication use process in order to advance into a sounder practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.182 | 0.386 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.029 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".