#3281 SAFETY AND EFFICACY OF VIRTUAL OUTPATIENT NEPHROLOGY CONSULTATION IN THE NORTHERN ALBERTA RENAL PROGRAM
Bibliographic record
Abstract
Abstract Background and Aims There is a growing awareness among Canadian health care providers of the need to incorporate virtual consultations safely and effectively post pandemic. With the onset of the Covid-19 pandemic in March 2020, outpatient consultation in Northern Alberta Renal Program (NARP) was rapidly transitioned to virtual delivery (telephone or videoconferencing) in place of face-to-face visits. To scale up and sustain virtual consultation in kidney care programs, its safety and effectiveness in improving processes of care (reduced wait time and access to care) and patient-related outcomes must be established. Data establishing safety and effectiveness of virtual consultation in kidney care has been limited. We therefore aimed to evaluate the safety and effectiveness of virtual consultation in patients with advanced CKD or peritoneal dialysis (PD) being cared for in NARP. Method The study was conducted in NARP (one of the largest kidney care programs in Canada). The study populations comprised two categories of patients with kidney disease: 1) CKD (non-dialytic) being followed in the ambulatory care clinics and 2) chronic PD patients being followed up in a dedicated home dialysis clinic at 3-monthly intervals. Data were collected over two-time points, pre and post implementation of virtual kidney following the COVID-19 pandemic: March 2019-February 2020 (pre-implementation), and March 2020- February 2021 (post implementation). We were only allowed to collate data on the processes of care outcomes for CKD (clinic cancellations or no-shows, wait times to see a nephrologist from the point of referral and number of visits), and adverse clinical outcomes (peritonitis rates, all-cause hospitalizations, technique failure, defined as PD failure with transition to hemodialysis) for the patients on PD. Summary statistics and tests of associations applied as appropriate. Interrupted time series analyses were used to evaluate trends. All analyses were conducted using (STATA 15 software (Stata Corporation, 2017). The study was approved by the University of Alberta Research Ethics Board. Results In patients on PD, the studied outcome measures were not significantly impacted (no changes in the trend) pre and post virtual care implementation (Figs 1a-c). The absolute number of clinic visits in patients on PD did not change (data not shown). In the patients with CKD, there were significant reductions in the rate of clinic cancellations/no-show rates (Fig. 2a), and a reduction in wait time (by a median of two weeks) following virtual care implementation (Fig. 2b). The rates of clinic visits pre and post implementation did not change (Fig. 2c) Conclusion The implementation of virtual consultation with the onset of COVID-19 pandemic (and the attendant reduction in face-to-face contacts with patients) did not negatively impact the care of patients on PD regarding risk for adverse clinical outcomes (peritonitis rates, all-cause hospitalizations, and technique failure). In patients with CKD, implementation of virtual care has led to significant improvements in the processes of care (reduction in wait times and enhanced access to care). These findings have implications in the design of sustainable virtual care programs for delivery of specialized kidney care in both dialysis and non-dialytic CKD in Canada and beyond.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".