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Transitioning from ‘blood’ safety to ‘transfusion’ safety: addressing the single biggest risk of transfusion

2011· article· en· W1513862438 on OpenAlexaff
Jeannie Callum, Yulia Lin, Alex Vieira Lima, Lisa L. Merkley

Bibliographic record

VenueISBT Science Series · 2011
Typearticle
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineBenchmarkingPatient safetyTransfusion medicineNear missBlood transfusionHuman errorMedical emergencyIntensive care medicineRisk analysis (engineering)SurgeryReliability engineeringBusinessEngineeringHealth care

Abstract

fetched live from OpenAlex

Transfusion errors occur at all points in the transfusion chain, often occurring at multiple points in the transfusion process for the same patient. Such events have been reported to national haemovigilance programs in almost all countries, over and over again. An incredible number of safety changes have been implemented to improve blood safety, including but not limited to: nucleic acid testing for HIV/HBV/HCV, bacterial culture for platelet concentrates, use of male‐only plasma, and the introduction of pathogen reduction strategies. By contrast, very little momentum has developed behind transfusion safety, in hope of improving the safe delivery of blood to patients. This article will review the interventions that have been studied by transfusion medicine services in attempt to improve transfusion safety at every link in the transfusion chain. The most important and indispensable safety step is the introduction of an error tracking system. Such a system should capture all deviations from standard operating procedures, including near‐misses that are captured before the blood product is issued. Near‐misses are 300‐fold more common and represent latent safety concerns requiring urgent attention. The system should be anonymous to ensure that there is no barrier to reporting and no‐fault to recognize that the vast majority of errors are due to latent system errors. The errors should be coded by type and location to allow for the ability to query the error database for the purposes of benchmarking and tracking and trending after system changes. Such a system will allow hospital transfusion services to focus their initiatives at the steps in the transfusion chain most in need of repair at their institution. The system changes that have been studied include: confirmatory group testing, computerized physician order entry, prospective screening of transfusion orders before/after issue, controlled patient registration, regional blood bank information systems, positive patient identification at time of sample collection and the start of transfusion (using barcode or RFID technology), controlled release refrigeration devices, patient involvement in the transfusion process, and healthcare professional education. For each area, the specific technologies or examples will be detailed, the reports from the literature will be reviewed, and the obstacles to implementation will be discussed. Now that blood safety has been assured, we need to re‐focus our attentions on the single biggest threat to patients: errors in the transfusion chain at the hospital level. We need to ensure that patients get blood only when required, that they get the correct product of the correct blood group, at the right dose, at the appropriate infusion rate, to the correct patient, at the right time. We need to take a rigorous scientific approach to solving transfusion safety to ensure that each process change is properly tested and validated to verify that each newly introduced process is safe and effective.

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)0.018
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.018
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.040
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.001
Science and technology studies0.0040.008
Scholarly communication0.0120.014
Open science0.0030.011
Research integrity0.0080.018
Insufficient payload (model declined to judge)0.0070.002

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 head0.047
GPT teacher head0.260
Teacher spread0.213 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations11
Published2011
Admission routes1
Has abstractyes

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