Evaluating the Implementation Strategy for Estimated Glomerular Filtration Rate Reporting in Manitoba: The Effect on Referral Numbers, Wait Times, and Appropriateness of Consults
Bibliographic record
Abstract
BACKGROUND: Chronic kidney disease screening using estimated glomerular filtration rate (eGFR) reporting is standard in many regions. With its implementation, many centres have had higher referral rates and increased wait times to see nephrologists. OBJECTIVE: Manitoba began eGFR reporting in October 2010. We measured the effect of eGFR reporting on referral rates, wait times, and appropriateness of referrals after an educational intervention. DESIGN: An interrupted time series design was used. SETTING: This study took place in Manitoba, Canada. PATIENTS: All referrals to the Manitoba Renal Program in the period prior to eGFR reporting between April 1, 2010 and September 30, 2010 were compared with a post period between January 1, 2011 and June 30, 2011. MEASUREMENTS: Data on demographics, co-morbidities, referral numbers and wait times were compared between periods. Appropriateness of consults was also measured after eGFR implementation. METHODS: Prior to eGFR reporting, primary care physicians underwent educational interventions on eGFR interpretation and referral guidelines. Referral rates and wait times were compared between periods using generalized linear models. Chart audits of a random sample of 232 patients in the pre period and 239 patients in the post period were performed. RESULTS: The pre and post eGFR reporting referral rate was 116 and 152 referrals/month, respectively. Average wait times in the pre and post eGFR reporting was 113 and 115 days, respectively. Non-urgent referral wait times increased by 40 days immediately post reporting, while urgent median referral wait times had a more gradual increase. Despite our intervention, inappropriate consultations post eGFR reporting was 495/790 (62.7%). LIMITATIONS: Our study did not measure the intervention's success on primary care providers, which may have affected our appropriateness data. Our time series design was not powered to find a statistically significant difference in referral numbers. Residual confounding of our results was possible given the retrospective nature of our study. CONCLUSION: Despite our educational intervention, the inappropriate referrals remained high, and wait times increased. Other systemic interventions should be considered to attenuate the potential negative effects of eGFR reporting and ensure timely access for patients needing specialist consultation.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".