#3281 SAFETY AND EFFICACY OF VIRTUAL OUTPATIENT NEPHROLOGY CONSULTATION IN THE NORTHERN ALBERTA RENAL PROGRAM
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,040 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».