Primary Care Provider Educational Tool to Improve Osteoarthritis Management in the Primary Care Setting
Bibliographic record
Abstract
Objectives Osteoarthritis is the most common form of arthritis, and its prevalence is increasing. As a non-inflammatory type of arthritis, it can be managed in the primary care setting with nonsteroid anti-inflammatory drugs, physical and occupational therapy, self-efficacy programs, and orthopedic referral for injections or surgery. At the University of Vermont Health Network, only 1 in 4 patients seen for joint pain have inflammatory arthritis,[1] and our wait time for new patients is 9 months. To address unnecessary referrals to rheumatology, we developed and evaluated an educational tool for differentiating non-inflammatory arthritis from inflammatory arthritis to improve provider comfort with managing osteoarthritis. Methods We conducted a chart review of adult patients diagnosed with osteoarthritis (n=390) at the University of Vermont Medical Center rheumatology clinic between 2020 and 2022 to gather data on referral patterns and outcomes. We used a pilot survey to assess primary care provider baseline comfort with diagnosing and treating osteoarthritis, which informed the creation of a two-page educational tool. The tool was introduced with a brief education session to primary care providers at 27 of 28 clinics in the network. The utility and impact of the tool was assessed with pre- (n=94) and post-intervention (n=32) surveys at 2 months. Results Osteoarthritis was diagnosed at the first visit in 390 patients referred to rheumatology for joint pain, accounting for 9% of all new patient referrals. Only 5% of these patients were seen again in rheumatology clinic and only 3% were found to have an inflammatory disease as a primary or concurrent diagnosis. Prior to the introduction of the tool, clinicians were least comfortable with knowing when to refer to rheumatology and most comfortable with diagnosing osteoarthritis. Post-intervention survey data indicated improvement in overall comfort with managing osteoarthritis (8% improvement, p=0.04) and knowing when to refer to orthopedics (10%, p=0.04). Half of clinicians (50%) reported using the tool in their practice, 63% described value in using the tool in medical education, and 43% in shared decision making. After using the tool, 34% anticipate a reduction in referrals to rheumatology and 9% have already referred fewer patients to rheumatology. Conclusion We developed and distributed an educational tool to primary care providers. Pre- and post-intervention surveys indicate that this was an effective approach for improving PCP comfort in the diagnosis and management of non-inflammatory joint pain with anticipation of fewer referrals to rheumatology resulting in improved access to care. [1.] Thoms BL. Rheumatol Adv Pract 2023;7(3):rkad067.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".