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Record W4405054375 · doi:10.1182/blood-2024-203534

Exploring Physician-Related Transfusion Errors Reported to the Transfusion Error Surveillance System (TESS) from 2016-2023: A Single Centre Study

2024· article· en· W4405054375 on OpenAlexaffabout
Julia Lou, Widad Abdulwahab, Matilda Cheung, Connie Colavecchia, H. G. Downie, Heather VanderMeulen, Jami‐Lynn Viveiros, Jane Yang, Yulia Lin, Akash Gupta

Bibliographic record

VenueBlood · 2024
Typearticle
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsCanadian Blood ServicesUniversity of TorontoHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineBlood transfusionTransfusion reactionEmergency medicineSurgery

Abstract

fetched live from OpenAlex

Introduction: Blood transfusions involve a multi-step process with an interdisciplinary team. Errors, defined as any deviation from established policies and standard operating procedures, can occur in any step of this process and may lead to adverse transfusion events. The web-based Transfusion Error Surveillance System (TESS) was implemented in Canada in 2005 and funded by the Public Health Agency of Canada to anonymously report and track error events. Previous studies have examined transfusion-related error types and frequency; however, an analysis of physician-related transfusion errors has not been performed. The purpose of this study was to describe physician-related transfusion errors in a tertiary care academic center, their harms and consequences, and how the COVID-19 pandemic may have influenced error trends. Methods: All transfusion error events are investigated, detailed and reported through TESS by trained blood bank laboratory technologists. These errors are categorized as those performed by the clinical or transfusion service. This was a retrospective study of errors reported to TESS from 2016 to 2023, focusing on clinical service transfusion errors and errors where the primary individual involved was a physician. Clinical service errors were categorized as involving sample collection (SC), sample handling (SH), product request (PR), request for pickup (RP), or unit transfusion (UT). SC errors are events related to sample collection; SH errors occur during the collection process but do not involve the sample itself; PR errors involve the incorrect ordering of blood or blood products for transfusion; RP errors involve the request to pick up blood or blood products from the transfusion service; and UT errors occur outside of the transfusion service and involve the storage, selection and administration of blood or blood product. The consequence of an error is classified as harm if the error leads to an adverse event including a transfusion reaction, delayed transfusion or under-transfusion. For error rates, the following denominators were used: SC and SH errors used total number of specimens received; PR errors used total number of blood components (BC) and fractionated products (FP) requested; and RP and UT errors used total number of BC and FP issued. Error rates were reported per 1000 of their respective denominator. Descriptive statistics were used to determine overall error rate trends across the years, types and consequences of errors, and details of patient harm cases. Results: Between January 2016 and December 2023, 29852 total errors were reported to TESS. 16155 (54%) were clinical service errors while 13697 (46%) were transfusion service errors. 2448 (8%) of the events reached the patient level, with 32 (0.1%) of these resulting in patient harm. SC had the highest error rate across all 8 years, ranging from 23 to 37 per 1000. In 2020, error rates for SC, SH, and PR increased. SC error rates decreased in 2021, while SH and PR decreased in 2022. Error rates for UT remained steady. RP errors increased in 2021 and remained consistently higher thereafter, increasing from 6 to 13 per 1000. While physician-related errors made up 9% (n=2672) of total errors, they accounted for 91% (29/32) of patient harm cases. Of the 29 patient harm cases, 27 (93%) were PR errors and 2 (7%) were UT errors. The consequences included 20 (69%) patients who had transfusion-associated circulatory overload (TACO), 3 (10%) who had febrile non-hemolytic transfusion reactions, 1 (3%) who had an allergic reaction, 1 (3%) who was hypotensive and 4 (14%) who had other adverse outcomes. Three patients died within 30 days of discovering the error; two deaths were not deemed attributable to the error. Conclusion: Physician-related transfusion errors have serious consequences. Despite making up only 9% of total errors, physician-related transfusion errors are responsible for 91% of cases that resulted in patient harm. Preliminary analysis suggests that harmful transfusion events such as TACO, could have been prevented (ex. by ordering diuretics or decreasing the rate of transfusion to high-risk patients). Further exploration into the patient harm cases will be conducted to identify areas of improvement for physicians. Using the TESS database to examine physician-related transfusion errors highlights the potential harm and preventable nature of these errors and offers opportunities to improve patient care.

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)0.008
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.992
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.030
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.004
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.055
GPT teacher head0.259
Teacher spread0.203 · 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.

Study designObservational
DomainMethods
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".

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Citations1
Published2024
Admission routes2
Has abstractyes

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