Electronic Advice Request System for Nephrology in Alberta: Pilot Results and Implementation
Bibliographic record
Abstract
BACKGROUND: Residents of rural areas of Alberta face significant barriers regarding access to specialist care, resulting in delays in provision of optimal care. Electronic referral and consultation systems are promising tools for facilitating timely access to specialist care, especially for people living in rural locations. OBJECTIVE: To report our initial experience with the launch of an electronic advice request system for ambulatory kidney care in Alberta, Canada. METHODS: We analyzed electronic advice requests for nephrology services in Alberta after the system's pilot launch, from October 2016 to December 2017. Data for province-wide advice request utility by primary care providers (PCPs) were extracted from Alberta Netcare for analysis. RESULTS: The total number of electronic advice requests directed to nephrology was 118 (mean number of requests: 2 per week). Only 31 (26.3%) of the cases required a face-to-face clinic visit with a nephrologist. Most (87; 73.7%) cases were managed by PCPs with ongoing nephrologist support via the advice request tool. Typical nephrologist response time was 5.7 ± 0.6 (mean ± SEM) days. CONCLUSION: These preliminary data suggest that the electronic advice request program has potential to enhance timely access to specialist kidney care and minimize unnecessary nephrologist visits while reducing response time. Broad implementation of this system may have a substantial positive impact on health outcomes and improve cost-effectiveness for nephrology care in the long term, particularly in rural communities of Alberta.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".