Descriptive statistics were calculated for proportions of eligible encounters, alert firing rate, suppression rate, distribution of response options to the alert, and ophthalmologist survey results. Ophthalmologist referral order rate by month was assessed using the χ2 test. The overall referral order patterns were compared by ophthalmologist gender. Patient and encounter characteristics, including alert firing criteria (visual acuity, ICD-10, or both), age, gender (female, male), race (white, black, Asian, other), ethnicity (Hispanic, non-Hispanic), clinic location (main hospital, satellite clinics), and visual acuity categories (≥20/40, <20/40 and ≥20/60, <20/60 and >20/200, ≤20/200 and >20/500, and ≤20/500), were extracted from the EHR and compared between encounters where the ophthalmologists did and did not order LVR referral. Patient encounter characteristics associated with ophthalmologist referral orders were explored using multilevel logistic regression models among those that met visual acuity criteria. Because the encounter-level referral response may not be independent within the same patient or the same ophthalmologist due to the patient-level characteristics and ophthalmologist referral practices, ophthalmologist and patient-level clustering effects on encounter-level referral responses were accounted for using mixed-effects logistic regression modeling. All analyses were conducted using STATA 15 (Stata Corp., College Station, TX).