Implementation and Optimization of Smart Infusion Systems: Are we Reaping the Safety Benefits?
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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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 | 0.026 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".