P.471: Quality improvement tools to manage organ donation processes: An instrumental case study.
Bibliographic record
Abstract
Objective: This study aims to analyze a non-conformity in the organ donation process, using a case from South Brazil, and develop a quality improvement tool to control the steps of organ donation, preventing future errors in donor management. Methods: An exploratory descriptive study of the experience report type was conducted, employing the instrumental case study approach proposed by Robert Yin. Additionally, the Ishikawa diagram and brainstorming technique were utilized to analyze non-compliance in organ donation and propose a quality tool for process improvement. Results: In a deceased organ donation case, the surgery to extract multiple organs proceeded smoothly. However, communication breakdowns led to inadequate family notification and body preparation, resulting in post-burial complaints. The analysis revealed discrepancies between documented and executed processes, prompting the development of a checklist for organ donation process verification. This checklist, tested in a pilot study and implemented in all donation processes, addresses notification, family communication, clinical assessments, documentation, and body preparation. Despite the irreversibility of the initial error, the study emphasizes the constructive use of quality tools for healthcare process analysis. The checklist addresses critical aspects, including notification protocols, family communication, clinical assessments, accurate documentation, and proper body preparation. The emphasis on communication, responsibility awareness, and effective procedural steps aligns with best practices in healthcare quality management. Conclusions: The study’ s outcome, a publicly available quality tool, allows for future implementation studies, assessing the checklist’ s effectiveness in preventing organ donation process failures in Brazil. Furthermore, the study advocates for a constructive approach to errors in healthcare, steering away from punitive actions that might negatively impact involved parties. The resulting checklist, made publicly available, holds the potential for further implementation studies. This allows for an evaluation of its effectiveness in preventing organ donation process failures, contributing to the enhancement of organ donation practices in Brazil and potentially serving as a valuable model for healthcare improvement worldwide.
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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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".