P3.1: Quality improvement tools to manage organ donation processes: an instrumental case study
Bibliographic record
Abstract
Introduction: Deceased organ donation is a highly complex and specific field that is often susceptible to adverse events. When unavoidable, failures in the donation process should be assessed to identify areas for improvements to be integrated into existing processes to reduce the probability of similar events in the future. Therefore, quality tools can help in the effective intervention of faulty processes. Using an anecdotal case based on the researchers’ previous experience, to analyze a non-conformity in the organ donation process and to develop a tool to control the steps of the organ donation process and prevent future errors. Methods: Instrumental Case Study evaluating an error that occurred during the return of the body of an organ donor to their family in a Brazilian hospital. We identified the mishaps of the donation process through the Ishikawa diagram detailing the process’s failure to establish improvement for future donation processes. Results: During the review of the donation process, we identified that verbal and written communication were the main causes of the error described. As a result of the case analysis, we developed a checklist to be included in the donor’s chart in future donation cases. This instrument was based on the experience of researchers and the clinicians involved, as well as current legislation on organ donation and determination of brain death, and on evidence-based maintenance protocols for potential donors. The checklist was submitted to the evaluation of a group of registered nurses who were specialists in the organ and tissue donation process for internal validation.Conclusion: This study enabled the creation of a quality improvement tool to control failures in the organ and tissue donation process. The public availability of such a tool will enable future implementation studies and evaluation of the checklist’s efficiency to prevent failures in the organ donation process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".