Transitioning from ‘blood’ safety to ‘transfusion’ safety: addressing the single biggest risk of transfusion
Notice bibliographique
Résumé
Transfusion errors occur at all points in the transfusion chain, often occurring at multiple points in the transfusion process for the same patient. Such events have been reported to national haemovigilance programs in almost all countries, over and over again. An incredible number of safety changes have been implemented to improve blood safety , including but not limited to: nucleic acid testing for HIV/HBV/HCV, bacterial culture for platelet concentrates, use of male‐only plasma, and the introduction of pathogen reduction strategies. By contrast, very little momentum has developed behind transfusion safety , in hope of improving the safe delivery of blood to patients. This article will review the interventions that have been studied by transfusion medicine services in attempt to improve transfusion safety at every link in the transfusion chain. The most important and indispensable safety step is the introduction of an error tracking system. Such a system should capture all deviations from standard operating procedures, including near‐misses that are captured before the blood product is issued. Near‐misses are 300‐fold more common and represent latent safety concerns requiring urgent attention. The system should be anonymous to ensure that there is no barrier to reporting and no‐fault to recognize that the vast majority of errors are due to latent system errors. The errors should be coded by type and location to allow for the ability to query the error database for the purposes of benchmarking and tracking and trending after system changes. Such a system will allow hospital transfusion services to focus their initiatives at the steps in the transfusion chain most in need of repair at their institution. The system changes that have been studied include: confirmatory group testing, computerized physician order entry, prospective screening of transfusion orders before/after issue, controlled patient registration, regional blood bank information systems, positive patient identification at time of sample collection and the start of transfusion (using barcode or RFID technology), controlled release refrigeration devices, patient involvement in the transfusion process, and healthcare professional education. For each area, the specific technologies or examples will be detailed, the reports from the literature will be reviewed, and the obstacles to implementation will be discussed. Now that blood safety has been assured, we need to re‐focus our attentions on the single biggest threat to patients: errors in the transfusion chain at the hospital level. We need to ensure that patients get blood only when required, that they get the correct product of the correct blood group, at the right dose, at the appropriate infusion rate, to the correct patient, at the right time. We need to take a rigorous scientific approach to solving transfusion safety to ensure that each process change is properly tested and validated to verify that each newly introduced process is safe and effective.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 tête enseignante, 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 ».