Implementation and Optimization of Smart Infusion Systems: Are we Reaping the Safety Benefits?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To address the high incidence of infusion errors, manufacturers have replaced the development of standard infusion pumps with smart pump systems. The implementation and ongoing optimization processes for smart pumps are more complex, as they require larger coordinated efforts with stakeholders throughout the medication process. If improper implementation/optimization processes are followed, hospitals invest in this technology while extracting minimal benefit. We assessed the processes hospitals employed when migrating from standard to smart infusion systems, and the extent to which they leveraged their investments from both a systems and resource perspective. Twenty-nine hospitals in Ontario, Canada, were surveyed that had either implemented smart pump systems or were in the process of implementing, representing a response rate of 69%. Results demonstrated that hospitals purchased smart pumps for reasons other than safety, did not involve a multidisciplinary team during implementation, made little effort to standardize drug concentrations or develop drug libraries and dosing limits, seldom monitored how nurses use the pumps, and failed to ensure wireless connectivity to upgrade protocols and download use data. Consequently, they are failing to realize the safety benefits these systems can provide.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 it