Peri-Operative Medication Errors in a Tertiary Care Teaching Hospital of a Low-Middle Income Country
Bibliographic record
Abstract
Abstract BackgroundIdentifying medication errors is one method of improving patient safety. Peri operative anesthetic management of patient includes polypharmacy and various steps prior to drug administration. Our objective was to analyze the medication errors reported in our critical incident reporting system (CIRS) database over the last 15 years (2004-2018) and to review measures taken for improvement based on the reported errors.MethodsAll Critical incidents (CI) reported during January 2004 till December 2018 were retrieved from CIRS database. Medication errors were identified and entered on a data extraction form which included reporting year, patients age, surgical specialty, ASA status, time of incident, phase and type of anesthesia and drug handling, type of error, class of medicine, level of harm, severity of adverse drug event (ADE) and steps taken for improvement.Results 311 medication errors were reported. Fifty two percent errors occurred in ASA II and III patient, and 43% during induction. Sixty % occurred during administration phase and 65 % were due to human error. Thirty seven percent were ADE, 58 of which were significant, 23 serious and five life-threatening errors. Majority errors involved neuromuscular blockers (32%) and opioids (13%).Conclusion Sharing of CI and a lesson to be learnt e-mail, colour coded labels, change in medication trolley lay out, decrease in floor stock and high alert labels were the low-cost steps taken to reduce incidents.Medication errors were more frequent during administration. Twenty eight percent resulted in significant, serious, or life-threatening events.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".