Blood supply management (<scp>RBC</scp>): definitions, description as a process, tools for assessment and improvement
Notice bibliographique
Résumé
Background A patient‐centered vision leads to conceive all transfusion medicine activities as a “blood supply chain”, starting with patients’ needs and ending with transfusion of needed blood components (BCs) to patients. This blood supply chain comprises two main sectors: the hospitals ‐ where transfusion is ordered by clinicians and administered to patients ‐ and the suppliers, acting from donor management to BC distribution, usually Blood Establishments (BEs).The aim is to help all involved actors to assess and improve their blood supply management (BSM) for the primary benefit of patients. Materials and methods Blood supply management, focusing on red blood cell concentrates (RBC), has been investigated by a working group (TS003 WG) of the European Committee (partial agreement) on blood transfusion (CD‐P‐TS), and also by an ISBT working party on BSM. From scientific literature, current members’ experiences and definitions of basic terms (use, demand, needs, self‐sufficiency, RBC supplier), the TS003 WG first designed BSM as a real process with the following steps. i) Assess past hospital RBC use for patients; ii) Establish a forecast for overall annual supply (BEs) and use (hospitals); iii) Establish annual blood collection program (BEs); iv) Weekly balance RBC use and supply in both BEs and hospitals; v) Review and update the patients’ RBC needs and their satisfaction. This process has been used as a basis to elaborate a questionnaire to investigate each step of the BSM process in the Council of Europe (CoE) countries, and CoE observers (Australia, Canada, New Zealand and USA) Results The most striking outcomes from the survey were as following. Of 45 surveyed countries, 39 (87%) responded. The blood supply chain (BSC) structures could be classified in three main types: National BE based, 100% hospital based and mix of different organisations. Information exchange between hospitals and RBC supplier(s) was frequently lacking. A national effective coordination of BSM could be found in countries with any of the three BSC organisations. A “vein to vein” IT system covering the entire BSC appeared to be of major importance to achieve such national coordination of BSM. The results of the study have been presented and discussed at a symposium organised by the CoE in October 2012. This provided an opportunity to evaluate the use of the TS003 questionnaire, combined with a SWOT (strengths, weaknesses, opportunities and threats) analysis, to self‐assess the current status of BSM in a given country and to deduce measures for improvement. This experience, deemed very fruitful by all participating countries, can be considered as a first validation of the proposed tool and method Conclusions The self‐assessment TS003 questionnaire combined with a SWOT analysis may be considered as an effective tool to evaluate current situation of BSM, deduce measures for improvement and assess their effectiveness. A complementary study by the ISBT BSM WP should help to further disseminate, evaluate and improve this tool and method in a larger number of countries, and gain additional knowledge to establish Good practices in BSM, for the primary benefit of patients, and also all other involved stakeholders.
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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 | 0,008 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,006 | 0,011 |
| Études des sciences et des technologies | 0,001 | 0,008 |
| Communication savante | 0,011 | 0,009 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,003 |
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 ».