Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States.
Owen Graduate School of Management, Vanderbilt University, Nashville, Tennessee, United States.
Appl Clin Inform. 2020 Oct;11(5):700-709. doi: 10.1055/s-0040-1716746. Epub 2020 Oct 21.
Suboptimal information display in electronic health records (EHRs) is a notorious pain point for users. Designing an effective display is difficult, due in part to the complex and varied nature of clinical practice.
This article aims to understand the goals, constraints, frustrations, and mental models of inpatient medical providers when accessing EHR data, to better inform the display of clinical information.
A multidisciplinary ethnographic study of inpatient medical providers.
Our participants' primary goal was usually to assemble a clinical picture around a given question, under the constraints of time pressure and incomplete information. To do so, they tend to use a mental model of multiple layers of abstraction when thinking of patients and disease; they prefer immediate pattern recognition strategies for answering clinical questions, with breadth-first or depth-first search strategies used subsequently if needed; and they are sensitive to data relevance, completeness, and reliability when reading a record.
These results conflict with the ubiquitous display design practice of separating data by type (test results, medications, notes, etc.), a mismatch that is known to encumber efficient mental processing by increasing both navigation burden and memory demands on users. A popular and obvious solution is to select or filter the data to display exactly what is presumed to be relevant to the clinical question, but this solution is both brittle and mistrusted by users. A less brittle approach that is more aligned with our users' mental model could use abstraction to summarize details instead of filtering to hide data. An abstraction-based approach could allow clinicians to more easily assemble a clinical picture, to use immediate pattern recognition strategies, and to adjust the level of displayed detail to their particular needs. It could also help the user notice unanticipated patterns and to fluidly shift attention as understanding evolves.
电子健康记录(EHR)中的信息显示不佳是用户面临的一个严重问题。由于临床实践的复杂性和多样性,设计有效的显示界面具有一定的难度。
本文旨在了解住院医疗服务提供者在访问 EHR 数据时的目标、限制、挫败感和心理模型,以便更好地为临床信息的显示提供依据。
对住院医疗服务提供者进行了一项多学科的民族志研究。
我们的参与者的主要目标通常是在时间压力和信息不完整的情况下,围绕给定的问题构建一个临床图景。为此,他们在思考患者和疾病时倾向于使用多层次抽象的心理模型;他们更喜欢立即进行模式识别策略来回答临床问题,如果需要,随后会使用广度优先或深度优先搜索策略;他们在阅读记录时对数据的相关性、完整性和可靠性很敏感。
这些结果与普遍的显示设计实践相冲突,后者将数据按类型(测试结果、药物、笔记等)进行分隔,这种不匹配增加了用户的导航负担和记忆需求,从而阻碍了有效的心理处理。一种流行且明显的解决方案是选择或过滤要显示的恰好被认为与临床问题相关的数据,但这种解决方案既脆弱又不受用户信任。一种不太脆弱的方法是使用抽象来概括细节,而不是过滤来隐藏数据。基于抽象的方法可以帮助临床医生更轻松地构建临床图景,使用即时模式识别策略,并根据其特定需求调整显示细节的级别。它还可以帮助用户注意到意外的模式,并随着理解的发展流畅地转移注意力。