A benchmarking program to reduce red blood cell outdating: implementation, evaluation, and a conceptual framework
Bibliographic record
Abstract
BACKGROUND: Benchmarking is a quality improvement tool that compares an organization's performance to that of its peers for selected indicators, to improve practice. STUDY DESIGN AND METHODS: Processes to develop evidence-based benchmarks for red blood cell (RBC) outdating in Ontario hospitals, based on RBC hospital disposition data from Canadian Blood Services, have been previously reported. These benchmarks were implemented in 160 hospitals provincewide with a multifaceted approach, which included hospital education, inventory management tools and resources, summaries of best practice recommendations, recognition of high-performing sites, and audit tools on the Transfusion Ontario website (http://transfusionontario.org). In this study we describe the implementation process and the impact of the benchmarking program on RBC outdating. A conceptual framework for continuous quality improvement of a benchmarking program was also developed. RESULTS: The RBC outdating rate for all hospitals trended downward continuously from April 2006 to February 2012, irrespective of hospitals' transfusion rates or their distance from the blood supplier. The highest annual outdating rate was 2.82%, at the beginning of the observation period. Each year brought further reductions, with a nadir outdating rate of 1.02% achieved in 2011. The key elements of the successful benchmarking strategy included dynamic targets, a comprehensive and evidence-based implementation strategy, ongoing information sharing, and a robust data system to track information. CONCLUSION: The Ontario benchmarking program for RBC outdating resulted in continuous and sustained quality improvement. Our conceptual iterative framework for benchmarking provides a guide for institutions implementing a benchmarking program.
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 | 0.122 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".