9 Rheum service: improving virtual care during COVID-19
Bibliographic record
Abstract
Background During COVID-19, rheumatology outpatients need timely access to care while social distancing. Video consults have potential to improve virtual assessments, however, some patients and providers are apprehensive about using this technology. Objectives Provide delightful and effective video consults for 90% of new patients by July 1. Methods We redesigned video appointments to create a seamless virtual experience. PDSA Series 1 identified improvement opportunities with a process map, fishbone, and driver diagram. PDSA Series 2 tested and implemented change ideas: digital appointment confirmations, reminders, and forms; video consults with limited pre-call testing; digital reports, requisitions, and messaging. PDSA Series 3 refined changes by decreasing reminders, increasing pre-call tests, and adding backup video platforms. Outcome measures were: 1)% consults by video, 2)% requesting more video appointments. Process measures were: 1) pre-call tests completed, 2) technical difficulties. Our balance measure was% virtual diagnoses modified after in-person visits. We collected data over ten weeks and emailed anonymized patient surveys one week after video consults. We analyzed data with run charts and descriptive statistics. Results We scheduled 135 new consults: 120 (89%) video, 14 (10%) phone, and 1 (1%) office. Twenty-one patients (16%) did not own a video-enabled device. Pre-visit, 12 patients (10%) participated in pre-call testing. Video consults were initiated for 97% of scheduled patients; of these, 6% suffered technical difficulties, requiring a switch to phone or another video platform. Surveys were completed after 40% (48/120) of video visits: 68% of patients wanted another video appointment; 28% were ‘not sure’; 4% declined. Virtual diagnoses stayed the same for 84% (32/38) of patients with follow-up in-person assessments. Conclusions While video consults proved effective for most patients, sociodemographic and technological barriers prevented others from participating. Next steps include improving access to video-enabled devices and providing more pre-visit training to reduce these barriers.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.004 |
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".