The impact of virtual rheumatology care on patient outcomes and hospital admissions: an ambispective study
Bibliographic record
Abstract
INTRODUCTION: The impact of virtual care on clinical outcomes, healthcare resources and long-term patient satisfaction will help to inform healthcare providers. We aimed to evaluate the impact of virtual rheumatology care on patients’ clinical outcomes and healthcare utilization. METHODS: Patients who had at least a phone-visit during the early-pandemic and enrolled in a previous survey were invited to attend this study. Through patient surveys and review of medical charts, patients’ clinical outcomes were collected, face-to-face visits in the pre-COVID-19 era (Jan 2019, 2020) and virtual care visits (VCV) in the pandemic period (Mar 2020-June 2021). RESULTS: Within 226 patients, the total number of rheumatology (median (IQR): 2 (2–3) vs. 3 [2, 3, 4], p < 0.001), emergency visits (19% vs. 29.3%, p:0.006) and hospital admissions (12.9% vs. 20.8%, p:0.015) due to any cause were increased during the pandemic, whereas there was no increased ER visit or admissions due to their rheumatological disease. Around 1/3 of patients reported being on more pain medication during the COVID-19-period. Failed VCV, requiring an additional in-person-visit within 60 days, was observed in 23 (8.3%) patients and 25 (3.1%) of 800 VCV. Close to 50% of the patients with failed-VCV were treated with additional steroid therapies during the pandemic. DISCUSSION: Our results support ongoing VCV with no increased healthcare utilization and a low rate of failed visits. These findings suggest that virtual care is here to stay for some patients and in some circumstances, and it is important to establish algorithms for implementing it to healthcare system. Our results provide evidence to inform insurers’ decision-making regarding virtual care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".