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Record W2984965143 · doi:10.1182/blood-2019-130615

Registration Errors Among Patients Receiving Blood Transfusions: A National Analysis from 2008-2017

2019· article· en· W2984965143 on OpenAlexaffabout
Shangari Vijenthira, Chantal Armali, H. G. Downie, Ann Wilson, Kathy Paton, Brian Berry, Christine Cserti‐Gazdewich, Jeannie Callum

Bibliographic record

VenueBlood · 2019
Typearticle
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsIsland HealthMcGill University Health CentreSunnybrook Health Science CentreHealth Sciences CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineBlood transfusionMedical recordEmergency medicineBlood productMedical emergencySurgery

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.933
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.012
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.013
GPT teacher head0.243
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes2
Has abstractyes

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