A Framework for Automating Psychiatric Distress Screening in Ophthalmology Clinics Using an EHR-Derived AI Algorithm

Abstract

Purpose: In patients with ophthalmic disorders, psychosocial risk factors play an impor-tant role in morbidity and mortality. Proper and early psychiatric screening can result in prompt intervention and mitigate its impact. Because screening is resource intensive, we developed a framework for automating screening using an electronic health record (EHR)-derived artificial intelligence (AI) algorithm. Methods: Subjects came from the Duke Ophthalmic Registry, a retrospective EHR database for the Duke Eye Center. Inclusion criteria included at least two encounters and a minimum of 1 year of follow-up. Presence of distress was defined at the encounter level using a computable phenotype. Risk factors included available EHR history. At each encounter, risk factors were used to discriminate psychiatric status. Model perfor-mance was evaluated using area under the receiver operating characteristic (ROC) curve and area under the precision–recall curve (PR AUC). Variable importance was presented using odds ratios (ORs). Results: Our cohort included 358,135 encounters from 40,326 patients with an average of nine encounters per patient over 4 years. The ROC and PR AUC were 0.91 and 0.55, respectively. Of the top 25 predictors, the majority were related to existing distress, but some indicated stressful conditions, including chemotherapy (OR = 1.36), esophageal disorders (OR = 1.31), central pain syndrome (OR = 1.25), and headaches (OR = 1.24). Conclusions: Psychiatric distress in ophthalmology patients can be monitored passively using an AI algorithm trained on existing EHR data. Translational Relevance: When paired with an effective referral and treatment program, such algorithms may improve health outcomes in ophthalmology.