Five-year sustainability of a de-implementation strategy to reduce inappropriate use of catheters: a multicentre, mixed-methods study
Bibliographic record
Abstract
Background: The use of peripheral intravenous catheters (PIVCs) contributes to healthcare-associated infections. In 2017, we implemented a multifaceted de-implementation strategy that successfully reduced the inappropriate use of catheters in seven hospitals in the Netherlands (RICAT-1 study). Five years later, we investigated the sustainability of this strategy and the contributing factors. Methods: Multicentre mixed-methods study (RICAT-2), consisting of an observational study and interviews in five hospitals in the Netherlands from May 2022 to June 2023. We screened adult patients with PIVCs admitted to internal medicine and non-surgical subspecialty wards. We excluded patients admitted for an elective short stay or terminally ill. Primary endpoint was the percentage of inappropriate PIVCs. We used logistic regression analyses to compare RICAT-2 to the RICAT-1 baseline data. We interviewed 18 healthcare professionals and managers involved in RICAT-1 and/or quality management. We combined thematic inductive analysis and framework analysis. Findings: In RICAT-1 baseline, we included 22.0% (282/1284) inappropriate PIVCs. In RICAT-2, we included 13.8% (154/1113) inappropriate PIVCs (odds ratio 0.76, 95% CI 0.68 to 0.84, p < 0.001). We observed no association between the number of maintained strategy components and the sustained effect. For most hospitals, a small temporary investment in a de-implementation strategy was sufficient to achieve sustained effects. The main facilitator for reducing inappropriate catheters was intrinsic motivation to reduce catheter-associated infections. Main barriers were other priorities, lack of time, and not having a dedicated clinical champion. Interpretation: Since inappropriate PIVC use was still lower after five years than before the de-implementation strategy, healthcare professionals should be encouraged to adopt this strategy. Funding: This project was funded by The Netherlands Organisation for Health Research and Development (project number: 839205002).
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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 | 0.046 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".