SUN-389 Optimizing the Referral Interaction Between Endocrinologists and Oculoplastics for Clinically Active Graves’ Orbitopathy: A Quality Improvement Project
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,033 | 0,033 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».