Infusion pump innovation: Embracing change for patients and bottom lines
Bibliographic record
Abstract
Within the infusion delivery landscape, significant room exists for an improved experience with more intuitive and interoperable solutions. The majority of smart infusion pumps still rely on technology developed more than a decade ago. Many Canadian healthcare institutions regularly undergo a comprehensive re-evaluation of infusion fleets, to modernize infusion delivery for patients across the country. Amid the availability of new technologies with evidence demonstrating their ability to elevate the current standards of care, this article argues for the need for healthcare systems to prepare for, and embrace, change when it comes to new technologies. Clinical informatics consultant, Helen Edwards, delves into why new technologies are needed now more than ever. She shares her experience with the Ivenix Infusion System, capturing how it can help redefine clinical workflows, reduce costs across the entire healthcare continuum, and better support patient care. She also offers insights on how to effectively introduce new technologies and cultivate an environment that is likely to be open and adaptive to the change. As the Canadian healthcare landscape continues to evolve, the proactive adoption of new technologies will be a step towards advancing the outcomes for patients and the sustainability of Canadian healthcare infrastructures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.035 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.029 |
| Scholarly communication | 0.032 | 0.015 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".