Improving appropriate use of intravenous albumin: results of a single-centre audit and multifaceted intervention
Bibliographic record
Abstract
BACKGROUND: Intravenous albumin has limited indications supported by randomised controlled trials, yet it is often prescribed for indications not supported by evidence. AIM: To reduce unnecessary transfusion of albumin. INTERVENTIONS: Under the leadership of a multidisciplinary quality improvement team, evidence-based recommendations were disseminated in tandem with a new electronic order set, an educational strategy, qualitative interviews with prescribers and a return policy change to reduce wastage. IMPLEMENTATION AND EVALUATION: Interventions were introduced in a staggered fashion. The primary outcome, appropriate use of albumin, was monitored and quantified using pre-intervention and post-intervention audits. Process measures included statistical process run charts of monthly usage of 5% and 25% albumin and wastage. Data on length of stay (hospital and intensive care), new inpatient starts on kidney replacement and mortality were collected as balancing measures. RESULTS: Appropriate albumin usage based on indication increased from 30% to 50% (p<0.0001). There was significantly less overall albumin usage in the post-intervention period compared with the pre-intervention period (negative coefficient, p<0.0001), driven by a major reduction in the utilisation of the 5% formulation (p<0.0001). Overall albumin usage was significantly lower in the post-intervention period, decreasing from 800 to 450 vials per month. The intervention resulted in significantly less wastage (negative coefficient, p=0.017). Mortality, length of stay and new starts on kidney replacement therapy remained constant throughout the study period. CONCLUSION: Improved prescribing of albumin was achieved with a multifaceted approach. Substantial and sustained reductions in usage were achieved without negatively impacting patient-important outcomes. The estimated annual savings for the purchase cost of albumin was CAN $300 000. We provide a structured process for other organisations to optimise their use of albumin.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".