The Importance of Medication Errors Reporting in Improving the Quality of Clinical Care Services
Bibliographic record
Abstract
INTRODUCTION: Medication errors have significant implications on patient safety. Error detection through an active management and effective reporting system discloses medication errors and encourages safe practices. OBJECTIVES: To improve patient safety through determining and reducing the major causes of medication errors (MEs), after applying tailored preventive strategies. METHODOLOGY: A pre-test, post-test study was conducted on all inpatients at a 177 bed hospital where all medication procedures in each ward were monitored by a clinical pharmacist. The patient files were reviewed, as well. Error reports were submitted to a hospital multidisciplinary committee to identify major causes of errors. Accordingly, corrective interventions that consisted of targeted training programs for nurses and physicians were conducted. RESULTS: Medication errors were higher during ordering/prescription stage (38.1%), followed by administration phase (20.9%). About 45% of errors reached the patients: 43.5% were harmless and 1.4% harmful. 7.7% were potential errors and more than 47% could be prevented. After the intervention, error rates decreased from (6.7%) to (3.6%) (P≤0.001). CONCLUSION: The role of a ward based clinical pharmacist with a hospital multidisciplinary committee was effective in recognizing, designing and implementing tailored interventions for reduction of medication errors. A systematic approach is urgently needed to decrease organizational susceptibility to errors, through providing required resources to monitor, analyze and implement effective interventions.
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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 | 0.024 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".