Supplementary Material for: An Integrated Kidney Care eConsult Practice Model: Results from the iKinect Project
Bibliographic record
Abstract
Background: Collaborative management of kidney disease relies on coordinated and effective partnerships between multiple provider teams. Siloed care contributes to limited access between physicians, resulting in delays in the diagnosis and treatment of kidney disease and inappropriate use of healthcare resources. These gaps contribute to dissatisfied and disempowered providers and patients. Digital systems such as eConsult can support collaborative management and address these gaps, thereby streamlining the consultation and referral process between primary care physicians (PCPs) and nephrologists. In this study, we evaluated an established eConsult platform integrated with a central triage process for a network of PCPs and nephrologists. The study aimed to assess the acceptability, feasibility, and impact on access to nephrology when using eConsult integrated into the management of kidney disease between PCPs and nephrologists. Methods: We conducted a 1-year pilot study and used mixed methods to measure the acceptability and feasibility of using eConsult for the management of kidney disease. We compared eConsult and traditional referrals with respect to types of consultation, referrals, and times to response to determine impact on access to kidney care. We conducted semi-structured interviews of PCPs and nephrologists to assess physician experience. Results: From January 8, 2018, to January 11, 2019, 52 PCPs and 23 nephrologists participated in the study, with 250 traditional referrals and 106 eConsults submitted during that period. The median response time for eConsult was 15 (3–64) h, with 25% originating outside the central Toronto region. The median time to first clinic appointment from a traditional referral was 4 months (111 [61–163] days). PCP and nephrologist interviews revealed high user satisfaction, citing efficiency and timely response as key facilitators. Conclusion: The eConsult platform was acceptable, feasible, and facilitated access to nephrology care compared to traditional referrals. Physicians report improvements in physician care delivery, nephrology care gaps, patient experience, and healthcare utilization.
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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 | 0.008 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.246 | 0.022 |
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".