Improving Time to Kidney Transplant Listing: A Single Center Quality Improvement Initiative
Bibliographic record
Abstract
Background: The process for kidney transplant listing is often lengthy and fragmented, contributing to delayed access to transplantation. Objective: At our center, we aimed to reduce time to listing by 25% within 12 months through a quality improvement (QI) initiative. Design: We conducted a mixed-method QI study. Setting: Tertiary care academic transplant center. Participants: Adult patients listed for transplantation from January 1, 2019, to July 31, 2023. Methods: Quantitative data were collected through chart review and qualitative data were gathered from semi-structured interviews with health care providers. Outcome measure was time from evaluation start to transplant listing and process measures included time to obtaining specific consultations and tests. Findings informed a multifaceted intervention, which included (1) improved documentation guidelines, (2) expedited access for delayed investigations, (3) a dedicated transplant nurse for Indigenous patients, and (4) an informatic-enabled coordination tool. Outcomes were then compared to those of patients listed from January 1 to July 31, 2024. Results: Among 109 patients in the preintervention cohort, the median time from evaluation to listing was 437 days, with only 9 patients listed predialysis. Indigenous patients, who represent over 25% of our population, accounted for only 11% of those listed. Key delays were identified in cardiology testing, colonoscopies, and mammograms. Ten health care providers were interviewed, and the main themes identified were: lack of resources, confusing task responsibilities, coordination of informatic systems and inequities affecting Indigenous patients. Following preliminary interventions, 22 patients were listed for transplant in just over 6 months-nearly one fifth of the total listed over the prior 4 years-demonstrating a marked acceleration in the listing process. Furthermore, median time to listing improved by 17% to 362 days, with a higher proportion of Indigenous patients (23%) listed and modest reductions in cardiology-related delays. Plan-Do-Study-Act cycles continue to optimize these interventions. Limitations: Conducted at a single center which limits the generalizability of findings to other health care centers. Furthermore, the timeline included the COVID-19 pandemic, which may have caused delays and influenced results independent of the QI initiative. Conclusions: A multifaceted intervention addressing local challenges showed early signs of success in reducing time to transplant listing and improving access for Indigenous patients. This highlights the critical role of data-driven QI initiatives in optimizing processes and improving patient care.
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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 | 0.037 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".