Supporting clinical reasoning through visual summarization and presentation of patient data: a systematic review.

IF 4.6 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Hao Fan, Angela Hardi, Po-Yin Yen
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引用次数: 0

Abstract

Objectives: Clinicians retrieve data from electronic health record (EHR) systems and summarize them into clinical information to accomplish clinical reasoning and decision-making tasks. Visualization, using meaningful summarization methods and intuitive presentation approaches, can enhance this process. This systematic review examines how EHR data are summarized, visualized, and aligned with the 7 clinical reasoning and decision-making tasks shared by clinicians.

Materials and methods: We searched 7 databases for research articles on individual patient EHR related to visualization, clinical decision-support, and patient summaries. Evidence from included studies was extracted for EHR data types, information summarization methods, visualization strategies, clinician characteristics, and evaluations. The synthesized evidence generated data-information-visualization (data-info-vis) flows.

Results: We included 112 studies of which 70 (62.5%) conducted detailed usability evaluations, while 42 (37.5%) did not report any evaluations. Gaps remain in deriving actionable insights from EHR data, particularly for tasks requiring data quality reports. Three representative data-info-vis flows emerge. The first uses structured data to generate patterns for temporal visualizations, supporting tasks such as diagnosis and patient management. The second abstracts data into miniature charts, aiding situation-aware understanding and knowledge synthesis. The third features high-level visual metaphors for complex and overarching tasks, such as achieving better care.

Discussion and conclusion: This review identifies 2 primary visualization strategies: (1) timeline-based presentations emphasizing temporal trends and longitudinal tracking, and (2) snapshot-based approaches focusing on status overviews and rapid assessments. The identified critical design approaches and distinct data-info-vis flows are tailored to clinical reasoning and decision-making tasks, offering insights for developing visualization-based decision-support tools.

通过患者数据的可视化总结和呈现来支持临床推理:系统回顾。
目的:临床医生从电子病历(EHR)系统中检索数据,并将其汇总为临床信息,以完成临床推理和决策任务。可视化,使用有意义的总结方法和直观的呈现方法,可以加强这一过程。本系统综述探讨了EHR数据如何汇总、可视化,并与临床医生共享的7项临床推理和决策任务保持一致。材料和方法:我们检索了7个数据库,检索了有关患者个人电子病历可视化、临床决策支持和患者总结的研究文章。从纳入的研究中提取EHR数据类型、信息汇总方法、可视化策略、临床医生特征和评估的证据。综合证据产生数据-信息-可视化(data-info-vis)流。结果:我们纳入了112项研究,其中70项(62.5%)进行了详细的可用性评估,而42项(37.5%)没有报告任何评估。在从电子病历数据中获得可操作的见解方面仍然存在差距,特别是对于需要数据质量报告的任务。出现了三种具有代表性的数据-信息可视化流程。第一种使用结构化数据生成时态可视化模式,支持诊断和患者管理等任务。第二种是将数据抽象成微型图表,帮助态势感知理解和知识综合。第三种是对复杂和总体任务的高级视觉隐喻,例如实现更好的护理。讨论和结论:本综述确定了两种主要的可视化策略:(1)基于时间轴的展示,强调时间趋势和纵向跟踪;(2)基于快照的方法,侧重于状态概述和快速评估。所确定的关键设计方法和独特的数据信息可视化流程是针对临床推理和决策任务量身定制的,为开发基于可视化的决策支持工具提供了见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of the American Medical Informatics Association
Journal of the American Medical Informatics Association 医学-计算机:跨学科应用
CiteScore
14.50
自引率
7.80%
发文量
230
审稿时长
3-8 weeks
期刊介绍: JAMIA is AMIA''s premier peer-reviewed journal for biomedical and health informatics. Covering the full spectrum of activities in the field, JAMIA includes informatics articles in the areas of clinical care, clinical research, translational science, implementation science, imaging, education, consumer health, public health, and policy. JAMIA''s articles describe innovative informatics research and systems that help to advance biomedical science and to promote health. Case reports, perspectives and reviews also help readers stay connected with the most important informatics developments in implementation, policy and education.
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