The Importance of Medication Errors Reporting in Improving the Quality of Clinical Care Services
Why this work is in the frame
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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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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 | 0.064 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it