SUN-389 Optimizing the Referral Interaction Between Endocrinologists and Oculoplastics for Clinically Active Graves’ Orbitopathy: A Quality Improvement Project
Bibliographic record
Abstract
Abstract Disclosure: M. Jay: None. P. Bapat: None. P. Palcu: None. X. Liang: None. Y. Alrodhan: None. W. Alsarhani: None. G. Nassrallah: None. J. Gilmour: None. Background: Graves’ orbitopathy (GO) affects roughly 40% of patients with Graves’ disease and can result in permanent vision loss if not promptly treated. For patients with clinically active GO (Clinical Activity Score [CAS] ≥ 3), timely and complete referrals to oculoplastic surgeons are critical for early intervention. Current referral processes often lack essential details, leading to delays in care. We aimed to increase the percentage of patients with clinically active GO assessed within 3 weeks of referral from a baseline of 30% to 70% by June 2025. Methods: The Model for Improvement framework for continuous quality improvement (QI) was employed. To understand the problem, a cross-sectional survey was distributed to 200 endocrinologists in our institution. The survey assessed referral practices, familiarity with oculoplastic specialists, access to diagnostic tools, and barriers to effective referrals. A baseline audit of oculoplastic referrals was then conducted between December 2024 and January 2025. The main outcome measure was the percentage of patients with a CAS ≥ 3 seen within 3 weeks of referral. Ethics board exemption was granted under QI guidelines. Results: Of the 200 endocrinologists surveyed, 30 (15%) responded. While 57% expected urgent GO referrals to be assessed within 1-2 weeks, 71% reported wait times exceeding one month. Only 4% of referrals included CAS, a key metric for identifying patients requiring urgent oculoplastic care, while 82% included thyroid function tests and TRAb levels, and 68% documented proptosis. Forty-one percent of respondents were unfamiliar with oculoplastic specialists in their area. Main barriers to effective referrals included unclear roles in managing GO (90%), lack of confidence in examining patients with GO (73%), and uncertainty regarding referral urgency (73%). Only 7% of respondents had high confidence in the referral process. Discussion: Gaps in referral completeness, wait times, and endocrinologist confidence were identified as priority areas for intervention. To address these gaps, targeted QI tools were implemented to create sequential change ideas: 1) focused education sessions on CAS and GO management, 2) development and implementation of a standardized referral form for GO, and 3) establishment of triage criteria to prioritize urgent cases. Outcome measures will be tracked using run charts, with process measures (e.g., percentage of referrals using the standardized form and meeting completeness criteria) and balancing measures (e.g., time to complete the referral form and percentage of referrals rejected by oculoplastics) evaluated through pre- and post-intervention audits. Stakeholder feedback will be integrated to refine processes and sustain improvements. The low survey response rate (15%) and single-institution audit may limit the generalizability but highlight areas requiring broader implementation. Presentation: Sunday, July 13, 2025
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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 | 0.033 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".