Implementation of a “Patient Blood Management” program in medium sized hospitals: Results of a survey among German hemotherapists
Bibliographic record
Abstract
Abstract Background and aims Germany uses more blood transfusions than the majority of other countries. The objective of this study was to detect the degree of Patient Blood Management (PBM) implementation within Germany and to identify obstacles to establishing PBM programs. Methods An electronical questionnaire containing 21 questions and 4 topics was sent in 2018 to the members of the German interdisciplinary hemotherapy (IAKH) society in Germany. The degree of PBM (described as pre‐, intra‐, postoperative period) was established via questions within the topics “management of preoperative anemia” (PA) (n = 5), “preoperative management and transfusion preparation” (n = 3), PBM organization and structure (n = 5), coagulation management (n = 3), perioperative transfusion performance and habits (n = 3), best practices and problems (n = 2). Results 533 German hospitals with transfusion activity received the questionnaire with a 32.5% response rate to the survey. A dedicated PBM program had not been established in a quarter of all small and medium sized institutions. Red blood cell transfusion was the only therapeutic option in a third of institutions. Approximately half of the hospitals did not use knowledge of PA rates or transfusion needs of surgical procedures. Institutions failed to implement PBM because of a lack of profit, workload, personnel shortage, and administrative support. Conclusion PBM was not present in at least a quarter of the hospitals interrogated. Factors for improvement were the relationship between health care disciplines and sectors, economic incentives, inclusion of relevant disciplines, and the structure of the blood industry. To improve BPM implementation, hospitals need support to implement top‐down PBM projects.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".