The impact of traditional and smart pump infusion technology on nurse medication administration performance in a simulated inpatient unit
Bibliographic record
Abstract
OBJECTIVE: Assess the impact of infusion pump technologies (traditional pump vs smart pump vs smart pump with barcode) on nurses' ability to safely administer intravenous medications. DESIGN: Experimental study with a repeated measures design. SETTING: High-fidelity simulated inpatient unit. RESULTS: The nurses remedied 60% of "wrong drug" errors. This rate did not vary as a function of pump type. The nurses remedied "wrong patient" errors more often when using the barcode pump (88%) than when using the traditional pump (46%) or the smart pump (58%) (Cochran Q=14.36; p<0.05). The number of nurses who remedied "wrong dose hard limit" errors was higher when using the smart pump (75%) and the barcode pump (79%) than when using the traditional pump (38%) (Cochran Q=12.13; p<0.003). Conversely, there was no difference in remediation of "wrong dose soft limit" errors across pump types. The nurses' pump programming was less accurate when mathematical conversions were required. Success rates on secondary infusions were low (55.6%) and did not vary as a function of pump type. CONCLUSIONS: These findings indicate that soft (changeable) limits in smart infusion pumps had no significant effect in preventing dosing errors. Provided that smart pumps are programmed with hard (unchangeable) limits, they can prevent dosing errors, thereby increasing patient safety. Until barcode pumps are integrated with other systems within the medication administration process, their role in enhancing patient safety will be limited. Further improvements to pump technologies are needed to mitigate risks associated with intravenous infusions, particularly secondary infusions.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".