P2.4: Quality improvement tools to manage deceased organ donation processes: a scoping review
Bibliographic record
Abstract
Background: Deceased organ donation, both after circulatory determination of death (DCD) and after neurological determination of death (NDD), is a highly complex and multi-phased process. To ensure a proper flow of the donation process, appropriate quality improvement tools should be used. These tools allow better control over health activities with more reliable and predictable outcomes. However, there is still a paucity of comprehensive evidence about the use of these tools to manage deceased donation processes. Therefore, this scoping review aims to summarize the international literature on quality improvement tools developed to manage deceased organ donation processes (DCD/NDD). Methods: Scoping review using the JBI methodology. Published literature was searched on MEDLINE, Embase, PsycINFO, CINAHL, Web of Science and Academic Search Complete from inception to July 2021 (updated in June, 2022). Unpublished and gray literature included reports from organ donation organizations. Reports were considered if they described the use of quality improvement tools to manage deceased donation processes in any healthcare setting. Data were screened, extracted and analyzed by two independent reviewers. Results: The first search yielded over 10000 citations and 40 were included in this review. Most reports were written in English (n=38), from Canada (n=21), and published between 2016 and 2022 (n=22). The tools identified included checklists, algorithms, flow charts, charts, pathways, decision tree maps and mobile apps. These tools were applied in the following phases of the organ donation process: (1) potential donor identification, (2) donor referral, (3) donor assessment and risk, (4) donor management, (5) withdrawal of life-sustaining measures,(6) death determination, (7) organ retrieval and (8) overall organ donation process.Conclusion: The existing evidence lacks details in the report of methods used for the development, testing and impact of these tools, and we could not locate tools specific to some phases of the organ donation process. Lastly, by mapping existing tools, we aim to facilitate both clinician choices among available tools, as well as research work building on existing knowledge. The authors would like to acknowledge the Canadian Donation and Transplantation Research Programme (CDTRP), Canadian Blood Services (CBS) and Children’s Hospital of Eastern Ontario (CHEO) for all their support and guidance in the development of this research. We also thank Robin Featherstone, Cochrane Information Specialist, for developing and employing the main electronic search strategies, and Amanda Ross-White, from JBI Centre of Excellence, for peer-reviewing the search strategy.
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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 | 0.042 | 0.146 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.035 | 0.035 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".