Implementation of an Automated Dispensing Cabinet System and Its Impact on Drug Administration: Longitudinal Study
Bibliographic record
Abstract
BACKGROUND: A technology that has been widely implemented in hospitals in the United States is the automated dispensing cabinet (ADC), which has been shown to reduce nurse drug administration errors and the time nurses spend administering drugs. OBJECTIVE: This study aimed to determine the impact of an ADC system on medication administration by nurses as well as safety before and after ADC implementation. METHODS: We conducted a 24-month-long longitudinal study at the National Taiwan University Hospital in Taipei, Taiwan. Clinical observations and questionnaires were used to evaluate the time differences in drug preparation, delivery, and returns in the inpatient ward by nurses before and after using the ADC. Drug errors recorded in the Medical Incident Events system were assessed the year before and after ADC implementation. RESULTS: The drug preparation time of the wards increased significantly (all P<.005). On average, 2 minutes of preparation time is needed for each patient. Only 1 unit showed an increase in the drug return time, but this was not significant. There were 9 (45%) adverse events during the drug administration phase, and 11 (55%) events occurred during the drug-dispensing phase. Although a decrease in the mean number of events reported was observed during the ADC implementation period, this difference was not significant. As for the questionnaire that were administered to the nurses, the overall mean score was 3.90; the highest score was for the item "I now spend less time waiting for medications that come from the pharmacy than before the ADC was implemented" (score=4.24). The item with the lowest score was "I have to wait in line to get my patient medications" (score=3.32). CONCLUSIONS: The nurses were generally satisfied with ADC use over the 9 months following complete implementation and integration of the system. It was acknowledged that the ADC offers benefits in terms of pharmaceutical stock management; however, this comes at the cost of increased nursing time. In general, the nurses remained supportive of the benefits for their patients, despite consequences to their workflows. Their acceptance of the ADC system in this study demonstrates this.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".