Developing an Ophthalmology Clinical Decision Support System to Identify Patients for Low Vision Rehabilitation.

Purpose: The purpose of this study was to develop and evaluate an electronic health record (EHR) clinical decision support system to identify patients meeting criteria for low vision rehabilitation (LVR) referral. Methods: In this quality improvement project, we applied a user-centered design approach to develop an interactive electronic alert for LVR referral within the Johns Hopkins Wilmer Eye Institute. We invited 15 ophthalmology physicians from 8 subspecialties to participate in the design and implementation, and to provide user experience feedback. The three project phases incorporated development evaluation, feedback analysis, and system refinement. We report on the final alert design, firing accuracy, and user experiences. Results: The alert was designed as physician-centered and patient-specific. Alert firing relied on visual acuity and International Classification of Diseases (ICD)-10 diagnosis (hemianopia/quadrantanopia) criteria. The alert suppression considerations included age < 5 years, recent surgeries, prior LVR visit, and related alert actions. False positive rate (firing when alert should have been suppressed or when firing criteria not met) was 0.2%. The overall false negative rate (alert not firing when visual acuity or encounter diagnosis criteria met) was 5.6%. Of the 13 physicians who completed the survey, 8 agreed that the alert is easy to use, and 12 would consider ongoing usage. Conclusions: This EHR-based clinical decision support system shows reliable firing metrics in identifying patients with vision impairment and promising acceptance by ophthalmologist users to facilitate care and LVR referral. Translational Relevance: The use of real-time data offers an opportunity to translate ophthalmic guidelines and best practices into systematic action for clinical care and research purposes across subspecialties.