Johns Hopkins Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Transl Vis Sci Technol. 2021 Mar 1;10(3):24. doi: 10.1167/tvst.10.3.24.
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.
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.
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.
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.
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.
本研究旨在开发和评估一种电子健康记录（EHR）临床决策支持系统，以识别符合低视力康复（LVR）转诊标准的患者。
在这项质量改进项目中，我们采用以用户为中心的设计方法，在约翰霍普金斯威尔默眼科研究所内开发用于 LVR 转诊的交互式电子提醒。我们邀请了来自 8 个亚专科的 15 名眼科医生参与设计和实施，并提供用户体验反馈。该项目分为三个阶段，包括开发评估、反馈分析和系统改进。我们报告最终的提醒设计、触发准确性和用户体验。
该提醒以医生为中心，以患者为特定对象。提醒触发依赖于视力和国际疾病分类（ICD-10）诊断（偏盲/象限盲）标准。提醒抑制的考虑因素包括年龄<5 岁、近期手术、既往 LVR 就诊和相关提醒操作。假阳性率（在应抑制提醒或未满足触发标准时触发）为 0.2%。总体假阴性率（在视力或就诊诊断标准符合时未触发提醒）为 5.6%。在完成调查的 13 名医生中，有 8 名医生认为该提醒易于使用，有 12 名医生会考虑持续使用。
这种基于 EHR 的临床决策支持系统在识别视力受损患者方面具有可靠的触发指标，并得到眼科医生用户的认可，有望促进护理和 LVR 转诊。
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