Improving Documentation Quality of Blood Transfusion Request Forms: A Two-Cycle Quality Improvement Initiative at a Tertiary Hospital in Sudan
Bibliographic record
Abstract
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.
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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 | 0.027 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".