Challenges and opportunities to prevent transfusion errors: a Qualitative Evaluation for Safer Transfusion (QUEST)
Bibliographic record
Abstract
BACKGROUND: One of the most frequent causes of transfusion-associated morbidity or mortality is the transfusion of the wrong blood to the wrong patient. This problem persists in spite of the incorporation of numerous procedures into the pretransfusion checking process in an effort to improve patient safety. A qualitative study was undertaken to understand this process from the perspective of those who administer blood products and to identify concerns and suggestions to improve safety. STUDY DESIGN AND METHODS: Twelve focus group discussions and seven individual interviews were conducted at six hospitals in five countries (n = 72 individuals). Health care professionals from a variety of clinical areas participated. Data analysis identified common themes using the constant comparison method. RESULTS: Five major themes emerged from the analysis: the pretransfusion checking process, training, policy, error, and monitoring. Findings include the following: staff were aware and appreciative of the seriousness of errors and were receptive to continuous monitoring, the focus was on checking the bag label with the paperwork rather than the bag label with the patient at the bedside, training methods varied with most perceived to have minimal effectiveness, and access to policies was challenging and keeping up to date was difficult. Other factors that could contribute to errors included high volume of workload distractions and interruptions and familiarity or lack of familiarity with patients. CONCLUSIONS: Multiple factors can contribute to errors during the pretransfusion checking limiting the effectiveness of any individual intervention designed to improve safety. Areas of further research to improve safety of blood administration were identified.
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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 | 0.057 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".