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Record W4411884116 · doi:10.3899/jrheum.2025-0314.98

Rheumatology Rapid Access Clinic: An Accelerated Access Model of Care

2025· article· en· W4411884116 on OpenAlexaffvenueabout
Trinette Kaunds, Nigil Haroon, B. Dexter, Katherine McQuaid-Bascon, Laura Passalent, Ahmed Omar

Bibliographic record

VenueThe Journal of Rheumatology · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHealthcare Systems and Technology
Canadian institutionsUniversity of TorontoUniversity Health NetworkMount Sinai Hospital
Fundersnot available
KeywordsMedicineRheumatologyEmergency departmentInternal medicineFamily medicineHealth careWorkforceMedical emergencyEmergency medicineNursing

Abstract

fetched live from OpenAlex

Objectives The Canadian healthcare system is well-regarded for its dedication to provide equitable care to all residents. Despite this commitment, concerns about wait times and access to care prevail, especially for patients transitioning from primary to specialized care.[1] In particular, wait times for rheumatology consultation can be lengthy.[2] In Ontario, the average wait time for assessment of suspected inflammatory arthritis is 74 days, far exceeding the benchmark target of 4 weeks.[3] Key drivers of longer wait times include the incongruity between rheumatology workforce supply and demand, which is further exacerbated by the increasing volume of patients. The lack of access to timely rheumatology consultation is predicted to worsen in the coming years2. To address this gap in access, a Rheumatology Rapid Access Clinic (RAC) led by an Advanced Clinic Practitioner in Arthritis Care (ACPAC) trained extended role practitioner (ERP) was established at Mount Sinai Hospital (MSH). A quality improvement project was undertaken to assess the impact of the RAC on timely access to care, reduction of emergency department readmissions, and the identification of diagnostic frequencies and subsequent absorption into the hospital’s Rheumatology Division. Methods Patients presenting to the MSH Emergency Department (ED) or Family Health Team (FHT), with suspected or confirmed rheumatologic conditions, as diagnosed by the attending practitioners/physicians, were referred to the RAC based on clinical need for expedited care. An ERP conducts a comprehensive assessment, including referring for appropriate laboratory tests and imaging per established medical directives. Thereafter, a treatment plan is initiated in collaboration with the attending rheumatologist. Results During the 10-month project period (September 2023 to July 2024), the RAC assessed a total of 96 patients. Of those referred, 94.1% attended their appointments. The majority of patients were female (59.4%). The average patient age was 50.0 years. The average time from referral to the initial appointment was 9.6 working days. Approximately 9.3% of the patients returned to the MSH ED with 3 patients re-referred from the RAC for medical concerns. Gout was the most frequently assessed condition, accounting for 18% of cases. 56% of the patients were integrated into the rheumatologists’ practice for ongoing care. Conclusion A Rheumatology RAC led by an ACPAC trained ERP facilitated timely and effective assessment and treatment planning for patients referred from primary or emergent care. This approach has the potential to optimize outcomes and could serve as a model for similar settings facing challenges with access to care. [1.] Bonello JP. University of Toronto Medical Journal 2023;100:29-3. [2.] Kwok TS. Clinical Rheumatology 2023;42(4):1205-1. [3.] Widdifield J. CMAJ Open 2016;4:E205-12.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.218

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0050.002
Open science0.0050.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0180.003

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.

Opus teacher head0.067
GPT teacher head0.354
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes3
Has abstractyes

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