Addressing the Quality Gap: An Order Set and Checklist to Improve Red Blood Cell Transfusion Ordering Practices on the Internal Medicine Ward
Bibliographic record
Abstract
Abstract Background: Recent guidelines, including ASH Choosing Wisely®, recommend the use of restrictive red blood cell (RBC) transfusion strategies. Our aim was to identify gaps in transfusion ordering practices among trainees and staff physicians on the internal medicine inpatient service, by performing an audit to determine compliance with hospital guidelines. This baseline study was then used to develop and implement preprinted orders and a transfusion checklist as an intervention to improve the quality of transfusion practice. Methods: We performed a single-center retrospective audit of all RBC transfusions ordered by trainees and staff physicians for patients admitted to general internal medicine over a 3-month period (June to August 2013). Compliance with institutional guidelines for transfusion indication and dose were ascertained. Secondary measures included documentation of informed consent, ordering of diuretics, and incidence of transfusion-related adverse events. These results guided the development of a checklist, which was implemented alongside evidence-based preprinted order sets in November 2013. The checklist specifically highlighted discussion of life-threatening transfusion risks, documentation of the informed consent process, and indications for pre-transfusion diuretics to prevent transfusion associated circulatory overload. The audit was repeated over a 3-month post-intervention period (November 2013 to January 2014) to assess for improvement. Comparison between the pre- and post-intervention groups was made using the chi-square test and Fisher’s exact test for categorical variables. Results: 90 transfusion orders in 63 patients were audited in the pre-intervention group, compared with 50 transfusion orders in 31 patients post-intervention; total inpatient days declined by 11.5% over the same period. 98.6% of transfusions were ordered by trainees and 1.4% by attending physicians. Baseline compliance for both indication and dose did not change (84.4% pre-intervention vs. 82.0% post-intervention, p = NS), and pre-transfusion hemoglobin was unchanged (69.0 g/L vs. 69.5 g/L). The frequency at which transfusion rate was specified increased after order sets were implemented (83.3% vs. 98.0%, p = 0.01). While the completion of consent forms was unchanged (98.4% vs. 100.0%, p = NS), explicit documentation of a risks and benefits discussion increased significantly (33.3% vs. 61.3%, p = 0.02). The frequency of appropriate diuretic administration increased (36.7% vs. 70.0%, p = 0.01) without increase in acute kidney injury or significant hypokalemia, and the proportion of diuretics ordered pre-transfusion increased (36.4% vs. 90.5%, p < 0.01). No adverse transfusion-related events occurred in either group. Conclusions: In this single-center study, there was good baseline compliance with transfusion guidelines within general internal medicine at our academic center. The development and implementation of preprinted orders and a checklist, based on gaps identified in the documentation of consent and the ordering of diuretics, significantly improved practices in these domains. These data suggest that preprinted orders and targeted checklists may be simple interventions that can be implemented to improve the quality of transfusion practice. Disclosures No relevant conflicts of interest to declare.
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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 | 0.051 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".