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Record W4415167307 · doi:10.7759/cureus.94553

Improving Documentation Quality of Blood Transfusion Request Forms: A Two-Cycle Quality Improvement Initiative at a Tertiary Hospital in Sudan

2025· article· en· W4415167307 on OpenAlexaff
Fakher Aldeen Raft Fakher Aldeen Noman, Romisaa Elamin, M. Osman, Ahmed Abdelaziz Abdelrahim Mohamed, Mohamed Yassin, Mohey Aldien Ahmed Elamin Elnour, A. A. Idris, Khadega Osman Abbas Alhadi, Ahmed Shakir Ali Yousif, Amjad Salah Ahmed Elhaj, Mohammed Ali Mohammed Ali, Z. Abdalla, Mohamed Abdelsalam Abdalla, Osama S Haroon, Esraa Elzubier Elshafie, Mohaned Altijani Abdalgadir Hamdnaalla, Abubakr E Musa, Mohammad Adam

Bibliographic record

VenueCureus · 2025
Typearticle
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDocumentationQuality managementAuditBlood transfusionPatient safetyQuality assuranceQuality (philosophy)

Abstract

fetched live from OpenAlex

BACKGROUND: Proper completion of blood transfusion request forms is vital for patient safety and quality assurance. Inadequate documentation increases the risk of transfusion errors, medico-legal complications, and compromised patient outcomes. Objective: To assess and improve the completeness and accuracy of blood transfusion request forms at Dongola Teaching Hospital through a two-cycle quality improvement initiative that included both evaluation and implementation phases. Methods: This two-cycle closed-loop clinical audit, conducted as a quality improvement project, incorporated retrospective and prospective elements and was guided by the Plan-Do-Study-Act (PDSA) model. In the first cycle, 50 transfusion request forms were reviewed (May 2025). Deficiencies were identified, and a standardized form was introduced with staff training. In the second cycle (September 2025), 42 forms were audited against the same benchmarks. Data were analyzed using descriptive statistics and comparative proportions with significance testing (p<0.05). Results: Significant improvements were observed across most documentation parameters. Patient demographics, clinical details, and laboratory information showed marked gains between the first cycle (n = 50) and the second cycle (n = 40): date of birth (35 [87.8%]), file number (39 [97.6%]), current Hb level (40 [100%]), and transfusion indication (33 [82.9%]). Laboratory documentation also improved substantially, including the collector's signature (39 [97.6%]) and collection date/time (39 [97.6%]). However, a slight decline was noted in the documentation of the number of units requested (34 [85.4%]). Conclusion: The intervention significantly improved form completeness and accuracy, enhancing transfusion safety and compliance with international standards. Continuous monitoring, regular feedback, and periodic re-audits are essential to sustain these improvements over time.

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)0.027
metaresearch head score (Gemma)0.022
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 score0.027
Threshold uncertainty score0.145

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0040.001
Scholarly communication0.0030.001
Open science0.0020.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.330
Teacher spread0.315 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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