A Usability Evaluation of an Infusion Pump by Nurses Using a Patient Simulator
Bibliographic record
Abstract
Using a high-fidelity patient simulator, this study had nurses evaluate the advanced features of an intravenous (IV) infusion pump being considered for purchase by a large Canadian health region. Three use cases or scenarios were developed, based on known difficulties administering multiple drugs through separate IV lines and the potential for certain drugs (e.g., heparin) to contribute to adverse outcomes in patients if the drug dosage was incorrectly calculated. After an in-service training session with the pump, thirteen nurses performed the use cases on an Emergency Care Simulator, which displayed a range of vital signs. During the sessions, nurses were required to use a think-aloud protocol, verbalizing all steps they were performing. The most common problems found were with the “Change Mode”and the “Select New Patient”features. Use of the On/Off switch was identified as a common strategy to clear pump information and to escape incorrect navigation paths. The consequential contribution to patient safety of these problems ranged from non-hazardous to potentially very hazardous. A number of design recommendations were made to address problems that were identified with the pump's hardware and software configurations, as well as to any in-service provided to new pump users.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".