University of Florida College of Nursing, Gainesville, FL, United States.
University of Florida Health Sciences Library, Gainesville, FL, United States.
J Am Med Inform Assoc. 2023 Dec 22;31(1):240-255. doi: 10.1093/jamia/ocad188.
Electronic health records (EHRs) user interfaces (UI) designed for data entry can potentially impact the quality of patient information captured in the EHRs. This review identified and synthesized the literature evidence about the relationship of UI features in EHRs on data quality (DQ).
We performed an integrative review of research studies by conducting a structured search in 5 databases completed on October 10, 2022. We applied Whittemore & Knafl's methodology to identify literature, extract, and synthesize information, iteratively. We adapted Kmet et al appraisal tool for the quality assessment of the evidence. The research protocol was registered with PROSPERO (CRD42020203998).
Eleven studies met the inclusion criteria. The relationship between 1 or more UI features and 1 or more DQ indicators was examined. UI features were classified into 4 categories: 3 types of data capture aids, and other methods of DQ assessment at the UI. The Weiskopf et al measures were used to assess DQ: completeness (n = 10), correctness (n = 10), and currency (n = 3). UI features such as mandatory fields, templates, and contextual autocomplete improved completeness or correctness or both. Measures of currency were scarce.
The paucity of studies on UI features and DQ underscored the limited knowledge in this important area. The UI features examined had both positive and negative effects on DQ. Standardization of data entry and further development of automated algorithmic aids, including adaptive UIs, have great promise for improving DQ. Further research is essential to ensure data captured in our electronic systems are high quality and valid for use in clinical decision-making and other secondary analyses.
为数据录入设计的电子健康记录 (EHR) 用户界面 (UI) 可能会影响 EHR 中捕获的患者信息的质量。本综述确定并综合了有关 EHR 中 UI 特征与数据质量 (DQ) 之间关系的文献证据。
我们通过在 2022 年 10 月 10 日完成的 5 个数据库中进行结构化搜索，对研究进行了综合回顾。我们应用 Whittemore 和 Knafl 的方法来识别文献、提取和综合信息，并进行迭代。我们采用 Kmet 等人的评估工具来评估证据的质量。该研究方案已在 PROSPERO（CRD42020203998）上注册。
符合纳入标准的研究有 11 项。研究了 1 个或多个 UI 特征与 1 个或多个 DQ 指标之间的关系。UI 特征分为 4 类：3 种数据捕获辅助工具和 UI 上的其他 DQ 评估方法。使用 Weiskopf 等人的措施评估 DQ：完整性（n=10）、正确性（n=10）和时效性（n=3）。必填字段、模板和上下文自动完成等 UI 特征可提高完整性或正确性或两者兼而有之。关于时效性的衡量标准很少。
UI 特征和 DQ 的研究很少，这突显了该重要领域知识的有限。所检查的 UI 特征对 DQ 既有积极影响，也有消极影响。数据录入的标准化和包括自适应 UI 在内的自动算法辅助工具的进一步发展，为提高 DQ 提供了很大的希望。进一步的研究对于确保我们的电子系统中捕获的数据具有高质量并可用于临床决策和其他二次分析是必要的。
