Park Heekyong, Wang Taowei David, Wattanasin Nich, Castro Victor M, Gainer Vivian, Murphy Shawn
Department of Research Information Science and Computing, Mass General Brigham, Somerville, Massachusetts, United States.
Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States.
Appl Clin Inform. 2024 Mar;15(2):250-264. doi: 10.1055/a-2269-0995. Epub 2024 Feb 15.
Timelines have been used for patient review. While maintaining a compact overview is important, merged event representations caused by the intricate and voluminous patient data bring event recognition, access ambiguity, and inefficient interaction problems. Handling large patient data efficiently is another challenge.
This study aims to develop a scalable, efficient timeline to enhance patient review for research purposes. The focus is on addressing the challenges presented by the intricate and voluminous patient data.
We propose a high-throughput, space-efficient HistoriView timeline for an individual patient. For a compact overview, it uses nonstacking event representation. An overlay detection algorithm, y-shift visualization, and popup-based interaction facilitate comprehensive analysis of overlapping datasets. An i2b2 HistoriView plugin was deployed, using split query and event reduction approaches, delivering the entire history efficiently without losing information. For evaluation, 11 participants completed a usability survey and a preference survey, followed by qualitative feedback. To evaluate scalability, 100 randomly selected patients over 60 years old were tested on the plugin and were compared with a baseline visualization.
Most participants found that HistoriView was easy to use and learn and delivered information clearly without zooming. All preferred HistoriView over a stacked timeline. They expressed satisfaction on display, ease of learning and use, and efficiency. However, challenges and suggestions for improvement were also identified. In the performance test, the largest patient had 32,630 records, which exceeds the baseline limit. HistoriView reduced it to 2,019 visual artifacts. All patients were pulled and visualized within 45.40 seconds. Visualization took less than 3 seconds for all.
HistoriView allows complete data exploration without exhaustive interactions in a compact overview. It is useful for dense data or iterative comparisons. However, issues in exploring subconcept records were reported. HistoriView handles large patient data preserving original information in a reasonable time.
时间线已用于患者回顾。虽然保持简洁的概述很重要，但复杂且大量的患者数据导致的合并事件表示带来了事件识别、访问模糊性和交互效率低下等问题。有效处理大量患者数据是另一项挑战。
本研究旨在开发一种可扩展、高效的时间线，以增强用于研究目的的患者回顾。重点是应对复杂且大量的患者数据带来的挑战。
我们为个体患者提出了一种高通量、节省空间的HistoriView时间线。为了实现简洁的概述，它使用非堆叠事件表示。叠加检测算法、y轴偏移可视化和基于弹出窗口的交互有助于对重叠数据集进行全面分析。部署了一个i2b2 HistoriView插件，使用拆分查询和事件缩减方法，在不丢失信息的情况下高效地提供完整历史记录。为了进行评估，11名参与者完成了可用性调查和偏好调查，随后提供了定性反馈。为了评估可扩展性，对100名随机选择的60岁以上患者在该插件上进行了测试，并与基线可视化进行了比较。
大多数参与者发现HistoriView易于使用和学习，无需缩放即可清晰地提供信息。所有人都更喜欢HistoriView而不是堆叠时间线。他们对显示、易于学习和使用以及效率表示满意。然而，也发现了挑战和改进建议。在性能测试中，最大的患者有32630条记录，超过了基线限制。HistoriView将其减少到2019个视觉元素。所有患者在45.40秒内被提取并可视化。可视化对所有患者来说都不到3秒。
HistoriView允许在简洁的概述中进行完整的数据探索，而无需进行详尽的交互。它对于密集数据或迭代比较很有用。然而，报告了在探索子概念记录方面的问题。HistoriView在合理的时间内处理大量患者数据并保留原始信息。
