Evaluation of Medication Errors in a Tertiary Care Hospital of a Low- to Middle-Income Country
Bibliographic record
Abstract
Introduction Medication errors (MEs) are a major public health concern as they are detrimental to patient safety, compromise patients' confidence in the healthcare system, increase healthcare costs, and adversely affect the patient's quality of life. This is especially true in low to middle-income countries where the significance of MEs is largely undervalued. This study aims to investigate the prevalence of MEs and analyze the causes, medicines involved, reporting, and severity of MEs in a tertiary care setting. Methods A prospective observational study was conducted from March 2020 to February 2021 in a tertiary care teaching hospital in South India. The data was collected after reviewing patient medical records, by interviewing patients and healthcare professionals. National Coordinating Council for Medication Error Reporting and Prevention (NCC MERP) index was used to evaluate MEs. Results A total of 557 MEs were identified from 3798 patients with a prevalence of 14.6%. Prescribing errors were the most commonly observed ME followed by errors related to documentation of medical records, administration-related errors, and dispensing errors. Lack of time for documentation of medication records, shift change and work overload were common causes of MEs. The majority of MEs were category A and B of the NCC MERP severity index. Conclusion Antibiotics and proton pump inhibitors were the most common medicines involved in MEs. Prescribing and documentation errors were most prevalent. Implementation of systems like strict adherence to treatment guidelines, computerized provider order entry (CPOE), barcode medication administration, and closed-loop electronic medication management systems may greatly help reduce MEs. All healthcare institutions should undertake routine audits to determine the prevalence and causes of medication errors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".