The British Columbia Nephrologists’ Access Study (BCNAS) – a prospective, health services interventional study to develop waiting time benchmarks and reduce wait times for out-patient nephrology consultations
Bibliographic record
Abstract
BACKGROUND: Early referral and management of high-risk chronic kidney disease may prevent or delay the need for dialysis. Automatic eGFR reporting has increased demand for out-patient nephrology consultations and in some cases, prolonged queues. In Canada, a national task force suggested the development of waiting time targets, which has not been done for nephrology. METHODS: We sought to describe waiting time for outpatient nephrology consultations in British Columbia (BC). Data collection occurred in 2 phases: 1) Baseline Description (Jan 18-28, 2010) and 2) Post Waiting Time Benchmark-Introduction (Jan 16-27, 2012). Waiting time was defined as the interval from receipt of referral letters to assessment. Using a modified Delphi process, Nephrologists and Family Physicians (FP) developed waiting time targets for commonly referred conditions through meetings and surveys. Rules were developed to weigh-in nephrologists', FPs', and patients' perspectives in order to generate waiting time benchmarks. Targets consider comorbidities, eGFR, BP and albuminuria. Referred conditions were assigned a priority score between 1-4. BC nephrologists were encouraged to centrally triage referrals to see the first available nephrologist. Waiting time benchmarks were simultaneously introduced to guide patient scheduling. A post-intervention waiting time evaluation was then repeated. RESULTS: In 2010 and 2012, 43/52 (83%) and 46/57 (81%) of BC nephrologists participated. Waiting time decreased from 98(IQR44,157) to 64(IQR21,120) days from 2010 to 2012 (p = <.001), despite no change in referral eGFR, demographics, nor number of office hrs/wk. Waiting time improved most for high priority patients. CONCLUSIONS: An integrated, Provincial initiative to measure wait times, develop waiting benchmarks, and engage physicians in active waiting time management associated with improved access to nephrologists in BC. Improvements in waiting time was most marked for the highest priority patients, which suggests that benchmarks had an influence on triaging behavior. Further research is needed to determine whether this effect is sustainable.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".