Patient, Rheumatologist and Therapist Perspectives on the Implementation of an Allied Health Rheumatology Triage (AHRT) Initiative in Ontario Rheumatology Clinics
Bibliographic record
Abstract
Laura M Fullerton,1 Sydney Brooks,2 Raquel Sweezie,2 Vandana Ahluwalia,3 Claire Bombardier,4 Anna R Gagliardi4 1Ontario Best Practices Research Initiative, Toronto General Research Institute, Toronto, ON, Canada; 2Ontario Division, Arthritis Society, Toronto, ON, Canada; 3Department of Rheumatology, William Osler Health System, Brampton, ON, Canada; 4Toronto General Research Institute, University of Toronto, Toronto, ON, CanadaCorrespondence: Sydney BrooksArthritis Society, 393 University Avenue, Suite 1700, Toronto M5G 3E6, CanadaTel +416 979-7228Fax +416 979-8366Email sbrooks@arthritis.caPurpose: The objective of this qualitative study was to explore patient, rheumatologist, and extended role practitioner (ERP) perspectives on the integration of an allied health rheumatology triage (AHRT) intervention in Ontario rheumatology clinics. Triage is the process of identifying the urgency of a patient’s condition to ensure they receive specialist care within an appropriate length of time. This research explores the clinical/logistical impact of triage by occupational and physical therapists with advanced arthritis training (ERPs), including facilitators and barriers of success, and recommendations for future application.Participants and Methods: Semi-structured telephone interviews were held with participating rheumatologists, ERPs, and a sample of patients from each clinical site (4 community, 3 hospital) in five Ontario cities. Interviews were audio-recorded and transcribed verbatim. Transcripts were analyzed using basic qualitative description. Two independent researchers compared coding and achieved consensus.Results: Patients (n=10), rheumatologists (n=6), and ERPs (n=5) participated in the study and reported reduced wait-times to rheumatology care, diagnosis, and treatment for those with inflammatory arthritis (IA). Rheumatologists and ERPs perceived that the intervention improved clinical efficiency and quality of care. Patients reported high satisfaction with ERP assessments, valuing early joint examination/laboratory tests, urgent referral if needed, and the provision of information, support, and management strategies. Facilitators of success included: supportive clinical staff, regular communication and collaboration between rheumatologist and ERP, and sufficient clinical space. Recommendations included extending ERP roles to include stable patient follow-up, and ERP care between scheduled rheumatology appointments.Conclusion: Findings support the integration of ERPs in a triage role in the community and hospital-based rheumatology models of care. Future research is needed to explore the impact of utilizing ERPs for stable patient follow-up in rheumatology settings.Keywords: health service needs and demand, rheumatic diseases, connective tissue disease, patient satisfaction
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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 | 0.020 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".