Evaluating Factors Associated With Telehealth Appropriateness in Outpatient Rheumatoid Arthritis Encounters Using the Encounter Appropriateness Score for You (EASY)
Bibliographic record
Abstract
OBJECTIVE: Telehealth has been proposed as a safe and effective alternative to in-person care for rheumatoid arthritis (RA). The purpose of this study was to evaluate factors associated with telehealth appropriateness in outpatient RA encounters. METHODS: A prospective cohort study (January 1, 2021, to August 31, 2021) was conducted using electronic health record data from outpatient RA encounters in a single academic rheumatology practice. Rheumatology providers rated the telehealth appropriateness of their own encounters using the Encounter Appropriateness Score for You (EASY) immediately following each encounter. Robust Poisson regression with generalized estimating equations modeling was used to evaluate the association of telehealth appropriateness with patient demographics, RA clinical characteristics, comorbid noninflammatory causes of joint pain, previous and current encounter characteristics, and provider characteristics. RESULTS: During the study period, 1823 outpatient encounters with 1177 unique patients with RA received an EASY score from 25 rheumatology providers. In the final multivariate model, factors associated with increased telehealth appropriateness included higher average provider preference for telehealth in prior encounters (relative risk [RR] 1.26, 95% CI 1.21-1.31), telehealth as the current encounter modality (RR 2.27, 95% CI 1.95-2.64), and increased patient age (RR 1.05, 95% CI 1.01-1.09). Factors associated with decreased telehealth appropriateness included moderate (RR 0.81, 95% CI 0.68-0.96) and high (RR 0.57, 95% CI 0.46-0.70) RA disease activity and if the previous encounters were conducted by telehealth (RR 0.83, 95% CI 0.73-0.95). CONCLUSION: In this study, telehealth appropriateness was most associated with provider preference, the current and previous encounter modality, and RA disease activity. Other factors like patient demographics, RA medications, and comorbid noninflammatory causes of joint pain were not associated with telehealth appropriateness.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".