评估生物医学可视化中的可用性：为空间组学及多学科研究平台重新设计启发式评估方法
Evaluating Usability in Biomedical Visualization: Rethinking Heuristic Evaluation for Spatial Omics and Multidisciplinary Research Platforms
- College of Medicine, University of Florida（佛罗里达大学医学院）
- Duke University（杜克大学）
- Kitware, Inc（Kitware公司）
- Washington University School of Medicine（华盛顿大学医学院）
- Indiana University School of Medicine（印第安纳大学医学院）
- Indianapolis VA Medical Center（印第安纳波利斯退伍军人事务医疗中心）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究针对空间组学等新兴技术驱动的生物医学可视化CRI平台，通过两项含39名参与者的研究，提出三种专用可用性启发式以优化其可用性与可访问性。
AI中文摘要：
引言：临床研究信息学（CRI）平台通过将高级计算工具整合到研究工作流中，支持生物医学发现。空间组学、人工智能赋能成像等新兴技术拓展了研究能力，但也引入了复杂界面，增加了认知负担并改变了既定分析流程。传统可用性框架可识别通用可用性问题，但往往遗漏高维生物医学数据特有的挑战。方法：开展两项互补研究，共39名参与者，以评估常规可用性启发式并确定CRI特有的标准。研究1包含19名完成交互式任务的本科生，研究2包含20名完成异步分层任务框架的临床专业人员。基于标准可用性启发式和新兴CRI特有的主题，采用演绎编码分析观察和访谈数据。结果：复杂数据叠加层与分析工具的同时呈现使用户不堪重负，尤其是空间组学经验有限的用户。参与者依赖试错式探索，在数据丰富环境中难以使用未标记工具。反馈显示，用户受益于分阶段入门、情境指导和渐进式功能引入，而非立即访问所有功能。讨论：高维研究平台需要超越传统框架的领域特定可用性标准。提出三种专门启发式：主动参数透明度、使用点指导和分阶段功能披露。这些启发式帮助开发者管理复杂性、提供情境支持，并提高多学科研究团队的可访问性。
英文摘要：
Introduction: Clinical research informatics (CRI) platforms support biomedical discovery by integrating advanced computational tools into research workflows. Emerging technologies such as spatial omics and AI-enabled imaging expand research capabilities but introduce complex interfaces that increase cognitive burden and alter established analytical processes. Traditional usability frameworks identify general usability issues but often miss challenges specific to high-dimensional biomedical data. Methods: We conducted two complementary studies involving 39 participants to evaluate conventional usability heuristics and identify CRI-specific criteria. Study 1 included 19 undergraduates completing interactive tasks, and Study 2 involved 20 clinical professionals completing an asynchronous hierarchical task framework. Observational and interview data were analyzed using deductive coding based on standard usability heuristics and emerging CRI-specific themes. Results: Simultaneous presentation of complex data overlays and analytical tools overwhelmed users, particularly those with limited spatial-omics experience. Participants relied on trial-and-error exploration and struggled with unlabeled tools in data-rich environments. Feedback indicated that users benefit from phased onboarding, contextual guidance, and progressive feature introduction rather than immediate access to all functionality. Discussion: High-dimensional research platforms require domain-specific usability criteria beyond traditional frameworks. We propose three specialized heuristics: Active Parameter Transparency, Point-of-Use Guidance, and Phased Feature Disclosure. These heuristics help developers manage complexity, provide contextual support, and improve accessibility for multidisciplinary research teams.
