Initiatives to Enhance the Quality of Referrals from Primary Care to Nephrology: A Systematic Review
Bibliographic record
Abstract
Background: Excessive referrals to nephrology are major determinants for prolonged wait times to access specialist kidney care. We conducted a systematic review of initiatives aimed at improving the quality of referrals to the nephrology service. Methods: Studies published from inception to April 1, 2021, that were designed to increase guideline concordant (GC) referrals or to reduce wait times and/or total referrals of adult patients with chronic kidney disease from primary care (PCP) to nephrology services were included. The primary outcomes of interest were changes to wait times, changes in the total number of referrals, and changes in the proportion of guideline-concordant referrals. The review was performed using a pre-specified protocol and reported using the PRISMA model. The results are reported based on taxonomy of interventions (provider education, provider reminder system, audit and feedback, multiple interventions, and other). Results: 27 studies met eligibility criteria, including 16 pre-post designs, 5 observational studies, 3 interrupted time series studies, and 2 randomized control trials. Among 6 studies that provided information on the relative change in total referrals after an intervention was applied, the proportionate change in total referrals was 15.3% [IQR: -16.7-80.6%]. 8 studies showed an increased trend in absolute number of referrals for the study periods with a median 23.2 [IQR, 22.0-56.2]. Among four pre-post design studies that reported the mean change in wait times, a significant reduction in the overall wait time was noted (median -26.3 [IQR, -104.2-1.6] days). Three studies used multiple interventions per each initiative, and two of these studies showed a relative increase by 11-fold in GC referrals pre and post intervention. Conclusions: Practice-based initiatives designed to improve the quality of referrals from PCP to nephrology services had different effects on their outcomes of interest. It appears that multifaceted interventions are more appropriate for a greater impact as no single intervention in our study showed a greater effect over another on reducing wait times, absolute number of referrals, or proportion of GC referrals.
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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 | 0.022 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".