Optimizing peripheral intravenous catheter insertion: A structured ultrasound device selection and education program quality initiative for intensive care unit nurses
Bibliographic record
Abstract
Background: Peripheral intravenous catheter (PIVC) failures remain common, hindering patient care. Ultrasound-guided PIVC (USG-PIVC) placement offers improved first-time and overall insertion success rates, reduced complications, and enhanced patient satisfaction. Yet, in our adult intensive care unit (ICU), most USG-PIVCs were placed by physicians, a skill that nursing staff could also benefit from acquiring. Purpose: Our aim was to select the most appropriate handheld ultrasound device and develop a comprehensive USG-PIVC nursing education program to improve PIVC success rates and enhance patient care. Methods: This quality improvement initiative’s literature review guided development of an education model and evaluation of three handheld ultrasound devices. Nurses received two hours of didactic training, two hours of classroom simulation, and supervised clinical practice. Outcomes were analyzed by the lead ICU clinical educator. Results: Five ICU nurses performed 76 USG-PIVC placements with 70%–90% overall insertion success rates, i.e., within two attempts. The preferred of three ultrasound devices was selected for its highest success rate, portability, transducer probe, and screen integration lending to ease of use, quick start-up, and clinical support. Nurses were overwhelmingly positive about the education program, as evidenced by informal qualitative feedback collected at each end of session. Conclusion: Our comprehensive USG-PIVC insertion program empowers nurses to improve vascular access through a structured approach, combining online learning, simulation, and supervised clinical practice, with effective ultrasound technology selection. This approach provides other organizations with insight to equip nurses and leverage ultrasound technology toward meeting the 2024 Canadian Vascular Access Association guidelines’ (in press) recommendations for ultrasound guidance and product selection.
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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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".