Key data elements for a successful pediatric rheumatology virtual visit: a survey within the PR-COIN network
Bibliographic record
Abstract
Introduction: Juvenile idiopathic arthritis (JIA) is the most common childhood rheumatic disease which is commonly monitored by a combination of history, physical examination, bloodwork, and imaging. The COVID-19 pandemic prompted a rapid shift to telemedicine to ensure that patients continued to receive healthcare. The shift to telemedicine changed the methodology and ability of healthcare providers to monitor their patients' progress, as they were unable to perform direct hands-on assessments. The following survey sought to understand the impact of switching pediatric rheumatology healthcare delivery from in-person to telemedicine modality. Specifically, it sought to examine the rate of collection of critical data elements (CDE) for monitoring JIA disease activity and outcomes, barriers and facilitators to its collection, opinions on difficulty and importance of collecting CDE over telemedicine, tools and electronic medical record modifications that facilitated CDE collection, and other data elements that were important to collect during telemedicine visits. Methods: A cross-sectional survey was sent to healthcare providers at all PR-COIN centers who saw patients using telemedicine. Qualitative data was analyzed using descriptive statistics and qualitative data was analyzed using an inductive approach. Results: Survey respondents reported that they documented the CDE at least 75% of the time. Barriers to assessing and documenting critical data elements included (1) the inability to palpate or visualize all joints over telemedicine, (2) connectivity issues, and (3) forgetfulness with collecting all CDE. Respondents suggested using reminders within the electronic medical record to prompt documentation completeness and improve reliability. They also suggested including medication adherence, quality of life, and patient/caregiver satisfaction with their telemedicine experience as part of their documentation. A few centers reported that they had established processes to assist with data collection in advance of the telemedicine visit; however, the variation in responses reflects the need to standardize the process of providing care over telemedicine. Discussion: Multiple barriers and facilitators to collecting CDE during telemedicine visits exist. Given that a proportion of the population will continue to be seen over telemedicine, teams need to adapt their practices to consistently provide high-quality care over virtual platforms, ensuring that patients at any institution receive a standardized level of service.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".