Registration errors among patients receiving blood transfusions: a national analysis from 2008 to 2017
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The key first step for a safe blood transfusion is patient registration for identification and linking to past medical and transfusion history. In Canada, any deviation from standard operating procedures in transfusion is an error voluntarily reportable to a national database (Transfusion Error Surveillance System [TESS]). We used this database to characterize the subset of registration-related errors impacting transfusion care, including where, when and why the errors occurred, and to identify frequent high-risk errors. MATERIALS AND METHODS: A retrospective analysis was conducted on transfusion errors reported to TESS by sentinel reporting sites relating to patient registration and patient armbands, between 2008 and 2017. Free-text comments describing the error were coded to further categorize into common error types. The number of specimens received in the transfusion laboratory was used as the denominator for rates to allow for comparison between hospital sites. RESULTS: Five hundred and fifty-four registration errors were reported from 10 hospitals, for a global error rate of 5·4/10 000 samples (median 5·0 [interquartile range 3·7-7·0]). The potential severity was high in 85·7% of errors (n = 475). The patient experienced a consequence in 10·8% of errors (n = 60), but none resulted in patient harm. Rates varied widely and differed by nature across sites. Errors most commonly occurred in outpatient clinics or procedure units (n = 160, 28·8%) and in emergency departments (n = 130, 23·5%). CONCLUSION: Registration errors affect transfusion at every step and location in the hospital and are commonly high risk. Further research into common root causes is warranted to identify preventative strategies.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".