Technology and medication errors: impact in nursing homes
Bibliographic record
Abstract
PURPOSE: The purpose of this paper is to study a medication distribution technology's (MDT) impact on medication errors reported in public nursing homes in Québec Province. DESIGN/METHODOLOGY/APPROACH: The work was carried out in six nursing homes (800 patients). Medication error data were collected from nursing staff through a voluntary reporting process before and after MDT was implemented. The errors were analysed using: totals errors; medication error type; severity and patient consequences. A statistical analysis verified whether there was a significant difference between the variables before and after introducing MDT. FINDINGS: The results show that the MDT detected medication errors. The authors' analysis also indicates that errors are detected more rapidly resulting in less severe consequences for patients. PRACTICAL IMPLICATIONS: MDT is a step towards safer and more efficient medication processes. Our findings should convince healthcare administrators to implement technology such as electronic prescriber or bar code medication administration systems to improve medication processes and to provide better healthcare to patients. ORIGINALITY/VALUE: Few studies have been carried out in long-term healthcare facilities such as nursing homes. The authors' study extends what is known about MDT's impact on medication errors in nursing homes.
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How this classification was reachedexpand
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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".