Proceedings of the 2019 Childhood Arthritis and Rheumatology Research Alliance (CARRA) Annual Scientific Meeting
Bibliographic record
Abstract
Background : Hydroxychloroquine (HCQ) is widely used to treat autoimmune diseases.Retinopathy from HCQ has been recognized for many years with estimated overall prevalence of 7.5%.Importantly, HCQassociated retinopathy is irreversible and may progress even after stopping the medication.In 2016, the American Academy of Ophthalmology (AAO) published revised recommendations of eye screening for patients receiving HCQ.Only 65% of patients receiving HCQ in our rheumatology practice were screened in concordance with these recommendations.We developed a quality improvement (QI) initiative which aims to increase the eye screening compliance in patients receiving HCQ and are seen in rheumatology clinic from 65% to 85% in 9 months and to sustain the improvement for 12 months.Methods : Patients who had HCQ on their list of medications followed in rheumatology clinic at Nationwide Children's Hospital from 1/01/2017 to 8/ 31/2017 were included in the baseline analysis.We formed a multidisciplinary team of rheumatologists, ophthalmologists, clinical pharmacist, clinic nurses, quality data technician and administrative staff.We formulated consensus eye screening guidelines based on the AAO recommendations and developed a risk-level-based algorithm (Figure 1).A key driver diagram was used to identify barriers to compliance and determine possible interventions.Major interventions included: 1) education of providers and patients, 2) weekly pre-visit planning (PVP) to identify at risk patients and provide individualized screening recommendations and 3) collaboration with ophthalmology to facilitate same day eye screening.Results : Baseline performance data included 328 patients.An average of 41 patients (range 30-51) were included in assessing the monthly performance (Figure 2).Although the primary aim was not met, the project has resulted in increased awareness and improved communication between rheumatology and ophthalmology providers.Barriers to compliance include: 1) provider and patient lack of knowledge, 2) obtaining accurate data reports to reflect the population of interest and 3) system barriers related to applying and tracking interventions in another specialty with different locations and EHR systems.Conclusions : This study highlights the importance of multidisciplinary team for QI projects that involve more than one department as well as the benefits of PVP in improving eye screening of patients receiving HCQ.Next steps will be directed at fine-tuning our interventions to target non-compliant patients and sharing provider-level data monthly to improve provider engagement.IRB-exempt.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".