Improving transfusion practice: ongoing education and audit at two tertiary speciality hospitals in Western Australia
Bibliographic record
Abstract
BACKGROUND: Institutions undertaking transfusion have a responsibility to ensure safe and appropriate practice. The hospital transfusion committee (HTC) plays a major role in monitoring all aspects of transfusion. Dedicated staff with the responsibility of undertaking transfusion education and audit have been employed at many hospitals. The question is 'Do these positions improve practice?'. STUDY DESIGN AND METHODS: In 2005, a transfusion coordinator was employed by the King Edward Memorial Hospital (KEMH) and Princess Margaret Hospital (PMH) in Perth, Western Australia. After an initial audit to collect baseline data on transfusion documentation and compliance with national guidelines, a series of interventions was undertaken. In addition, the transfusion protocols were rewritten and published electronically. Further audits were undertaken in 2006, 2007 and 2009. RESULTS: Sequential audits show measured improvements in transfusion documentation. Baseline, hourly and completion observations are now correctly recorded in >94% of records at KEMH and >96% of records at PMH. Compliance with recording of 15 min observations has shown a 23% magnitude increase at KEMH and 36% at PMH. Compliance with recording of consent has increased by 20% at KEMH and 31% at PMH. Promotion of positive patient identification, when collecting specimens and administering blood, has been undertaken. CONCLUSION: The initiatives implemented by the transfusion coordinator and endorsed by the HTCs have improved the standard of transfusion documentation and practice at both institutions.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".