Telerheumatology Shared-Care Model: Leveraging the Expertise of an Advanced Clinician Practitioner in Arthritis Care (ACPAC)-Trained Extended Role Practitioner in Rural-Remote Ontario
Bibliographic record
Abstract
OBJECTIVE: A shortage of rheumatologists has led to gaps in inflammatory arthritis (IA) care in Canada. Amplified in rural-remote communities, the number of rheumatologists practicing rurally has not been meaningfully increased, and alternate care strategies must be adopted. In this retrospective chart review, we describe the impact of a shared-care telerheumatology model using a community-embedded Advanced Clinician Practitioner in Arthritis Care (ACPAC)-extended role practitioner (ERP) and an urban-based rheumatologist. METHODS: A rheumatologist and an ACPAC-ERP established a monthly half-day hub-and-spoke-telerheumatology clinic to care for patients with suspected IA, triaged by the ACPAC-ERP. Comprehensive initial assessments were conducted in-person by the ACPAC-ERP (spoke); investigations were completed prior to the telerheumatology visit. Subsequent collaborative visits occurred with the rheumatologist (hub) attending virtually. Retrospective analysis of demographics, time-to-key care indices, patient-reported outcomes, clinical data, and estimated travel savings was performed. RESULTS: Data from 124 patients seen between January 2013 and January 2022 were collected; 98% (n = 494/504 visits) were virtual. The average age of patients at first visit was 55.6 years, and 75.8% were female. IA/connective tissue disease (CTD) was confirmed in 65% of patients. Mean time from primary care referral to ACPAC-ERP assessment was 52.5 days, and mean time from ACPAC-ERP assessment to the telerheumatology visit was 64.5 days. An estimated 493,470 km of patient-related travel was avoided. CONCLUSION: An ACPAC-ERP (spoke) and rheumatologist (hub) telerheumatology model of care assessing and managing patients with suspected IA in rural-remote Ontario was described. This model can be leveraged to increase capacity by delivering comprehensive virtual rheumatologic care in underserved communities.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".