Barriers and Facilitators to Implementation of Intravenous Cardiovascular Treatments in Ambulatory Settings
Bibliographic record
Abstract
AIMS: Intravenous (IV) therapies have transformed the management of various cardiovascular conditions in ambulatory patients. However, uptake of these therapies in ambulatory care settings has several barriers. In this systematic scoping review, we aimed to identify the barriers and facilitators that influence the implementation of current IV therapies in ambulatory settings. METHODS: We searched MEDLINE, Embase and CINAHL databases from inception to September 2023 for studies on barriers and facilitators of IV therapy uptake in ambulatory patients. We classified the identified factors and performed a thematic analysis. RESULTS: Fifteen studies, primarily conducted in North America and Europe, were included. Methodologies varied, precluding quantitative synthesis. Key barriers were identified across several levels. At the medication level, barriers included the need for multiple vials and lengthy preparation. Patient-level barriers included adverse effects, infections, painful venous access and non-adherence. Clinician-level barriers included understaffing, time constraints and safety concerns. Institutional barriers ranged from staff or equipment shortages to liability concerns and complex logistics. Healthcare system barriers included financial constraints and limited care delivery services. Facilitators included evidence-based indications, patient education and comfort, staff experience, guidance documents, safe settings, favourable insurance policies and supportive guidelines. CONCLUSIONS: As novel IV treatments emerge, addressing barriers and leveraging facilitators preemptively can enhance the successful implementation of IV therapies and improve clinical outcomes in ambulatory settings.
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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 | 0.036 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".