Addressing the Quality Gap: An Order Set and Checklist to Improve Red Blood Cell Transfusion Ordering Practices on the Internal Medicine Ward
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,051 | 0,130 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».