Frequency and Type of Medication Discrepancies in One Tertiary Care Hospital
Bibliographic record
Abstract
Background/Objective: Discrepancies in records used within the medication use system have been identified as a contributing factor of medication errors.The objective of this study was to determine the frequency and type of discrepancies in the medication use system in one tertiary care hospital.Methods: Using a sample of patients (convenience sampling technique), the physician's orders, the nursing medication administration record and the pharmacy profile were compared in an attempt to identify discrepancies among them.A discrepancy was defined as a deviation from the physician's order as written in the chart.Each discrepancy was categorized according to seven components of the medication order, its location in the medication use process and its mode of delivery.Results: One thousand, four hundred twenty-four orders representing 197 patients from 13 nursing units were sampled for this study.Thirteen percent of the orders were discrepant and 61% of patients had at least one discrepancy.The most frequent types of discrepancies were drug omissions and unordered drugs.Discussion: The discrepancies identified in this study suggest that either orders are not reaching pharmacy or orders are not being processed appropriately in pharmacy.The location of discrepancies also suggests that there are deficiencies in communication between healthcare professionals. Background/ObjectiveThe safety of the medication use system (MUS) is an issue which is a concern in many healthcare organizations today.This problem was clearly identified in the Institute of Medicine's report To Err Is Human (Kohn et al. 1999).The Canadian Adverse Events Study (Baker et al. 2004) helped to quantify the magnitude of this problem in the Canadian inpatient environment.In that study, almost one-quarter of all the adverse events identified were drug-or fluid-related.The annual cost of preventable drug-related morbidity and mortality in Canada has been estimated to be $11 billion per year in older adults alone (Kidney and MacKinnon 2001).Problems related to documentation and communication in the MUS are commonly cited in studies.Of 134 patients in the intervention arm of a study (Nickerson et al. 2005) at the Moncton Hospital, NB, 96.3% (129) patients had at least one drug-therapy problem for monitoring, while 39.6% (53) patients had a drug-therapy inconsistency or omission.All of these problems were identified just prior to discharge from hospital.In another study that focused on unintended discrepancies on admission, 53.6% of patients had at least one such discrepancy (Cornish et al. 2005).Current efforts by the Canadian Council on Health Services Accreditation (2006) and the Safer Healthcare Now! (2006) campaign directed toward Frequency and Type of Medication Discrepancies in One Tertiary Care Hospital
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".