Improving Hydroxychloroquine Dosing and Toxicity Screening at a Tertiary Care Ambulatory Center: A Quality Improvement Initiative
Bibliographic record
Abstract
OBJECTIVE: Hydroxychloroquine (HCQ) is a commonly used weight-based medication with a risk of retinal toxicity when prescribed at doses above 5 mg/kg/day. The objectives of our study were (1) to characterize the frequency of inappropriate HCQ dosing and retinopathy screening, and (2) to improve guideline-based management by implementing quality improvement (QI) strategies. METHODS: A retrospective chart review was performed to obtain baseline analysis of HCQ dosing, weight documentation, and retinal toxicity screening to characterize current practices. The primary aim was to increase the percentage of patients appropriately dosed from 30% to 90% over a 10-month period. The secondary aim was to increase the percentage of documented retinal screening from 59% to 90%. The process measure was the number of patients with a documented weight in the chart. The balancing measure was the physician's perceived increase in time spent with each patient due to implemented interventions. QI methodology was used to implement sequential change ideas: (1) HCQ weight-based dosing charts to facilitate prescription regimens; (2) addition of scales to patient rooms to facilitate weight documentation; and (3) electronic medical record (EMR) "force function" involving weight documentation and autodosing prescription. RESULTS: The percentage of patients being weighed increased from 40% to 92% after 10 months. Appropriate HCQ dosing improved from 30% to 89%. Retinal screening documentation improved by 33%. CONCLUSION: Dosing charts in clinic rooms, addition of weight scales, and EMR force function autodosing prescriptions significantly improved appropriate HCQ dosing practices. These interventions are generalizable and can promote safe and guideline-based 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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".