Registration Errors Among Patients Receiving Blood Transfusions: A National Analysis from 2008-2017
Bibliographic record
Abstract
BACKGROUND: The blood transfusion chain is complex and error-prone. The key first step is patient registration for identification and linking to past medical and transfusion history. In Canada, transfusion-related errors are voluntarily reported to a national web-based database (Transfusion Error Surveillance System [TESS]). This project focuses specifically on the subset of registration errors impacting transfusion care in Canada. OBJECTIVE: To characterize registration-related transfusion errors in Canada between 2008 and 2017, including where, when, and why the errors occurred METHODS: A retrospective study was conducted on transfusion errors reported to TESS between January 2008 and December 2017. Errors reportable to TESS are defined as any deviation from standard operating procedures. Errors relating to patient registration and patient armbands were extracted. Data were available from 26 sentinel sites in three provinces. Volume of specimens received in the transfusion laboratory was used as denominator data where available. RESULTS: 554 errors pertaining to registration or patient armbands were reported, for a global error rate of 3.4/10,000 (range 0-18, median 1.4 [IQR 0-5.9]) (Figure 1). Errors were typically discovered before laboratory sample testing (n=222, 42%); 17% were discovered after product issue but before infusion (n=90). Errors most commonly occurred in outpatient clinics or procedure units (30% [range 0-100%]) and in emergency departments (24% [0%-100%]). The patient experienced a consequence in 11% (0%-75%) of errors but none resulted in transfusion reactions. The most frequent reports were name errors (29% [0%-100%]), duplicate patient registrations (27% [0%-100%]), and missing armbands (10% [0%-75%]) (Figure 2). CONCLUSION: Registration errors affect transfusion at every step and location in the hospital. Rates vary widely and differ by nature across sites. Further research into drivers of this heterogeneity is warranted to identify best preventative practices. This work was supported by the Public Health Agency of Canada and the Canadian Blood Services Program Support Award. Figure 1. Rate of error was calculated for sites with available denominator data from 2008 to 2015. Figure 2. Errors were categorized by site and by type: duplicate registration (multiple records existing for a single patient), doppelganger (confusion between records of two or more patients with similar identifiers), comingling (two or more patients using the same medical record file), missing patient armband, incorrect patient armband, name error (misspelled, missing, or incorrect), wrong date of birth, wrong sex, patients presenting another person's identification at registration, mistyping of medical record numbers, incorrect medical record numbers, errors in provincial health insurance numbers, and other errors. Numerical totals of errors at each site are given. Disclosures No relevant conflicts of interest to declare.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".