BACKGROUND Visualization is important to aid practitioners in understanding local care processes and drive quality improvement (QI). Important aspects include timely feedback and ability to plot data over time. Moreover, the complexity of care also needs to be understood, as it affects the variation of care processes. However, there is a lack of QI methods visualizing multiple, related factors such as diagnosis date, death date, and cause of death to unravel their complexity, which is necessary to understand processes related to survival data. Lexis diagrams visualize individual patient processes as lines and mark additional factors such as key events. This study explores the potential of Lexis diagrams to support QI through survival data analysis, focusing on feedback, timeliness, and complexity, in a gynecological cancer setting in Sweden. METHODS Lexis diagrams were produced based on data from a gynecological cancer quality registry (4481 patients). The usefulness of Lexis diagrams was explored through iterative data identification and analysis through semi-structured dialogues between the researcher and domain experts (clinically active care process owners) during five meetings. Visualizations were produced and adapted by the researcher between meetings, based on the dialogues, to ensure clinical relevance, resulting in three relevant types of visualizations. RESULTS Domain experts identified different uses depending on diagnosis group and data visualization. Key results include timely feedback through close-to-real-time visualizations, supporting discussion and understanding of trends and hypothesis-building. Visualization of care process complexity facilitated evaluation of given care. Combined visualization of individual and population levels increased patient focus and may possibly also function to motivate practitioners and management. CONCLUSION Lexis diagrams can aid understanding of survival data, triggering important dialogues between care givers and supporting care quality improvement and new perspectives, and can therefore complement survival curves in quality improvement.

中文翻译：

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探索 Lexis 图对于质量改进的有用性。


背景技术可视化对于帮助从业者理解本地护理流程并推动质量改进（QI）很重要。重要的方面包括及时反馈和随时间绘制数据的能力。此外，还需要了解护理的复杂性，因为它会影响护理过程的变化。然而，缺乏可视化多个相关因素（如诊断日期、死亡日期和死亡原因）的 QI 方法来揭示其复杂性，而这对于理解与生存数据相关的过程是必要的。 Lexis 图将单个患者流程可视化为线条，并标记关键事件等其他因素。本研究探讨了 Lexis 图在瑞典妇科癌症环境中通过生存数据分析支持 QI 的潜力，重点关注反馈、及时性和复杂性。方法 Lexis 图是根据妇科癌症质量登记处（4481 名患者）的数据制作的。在五次会议期间，研究人员和领域专家（临床主动护理流程负责人）之间进行半结构化对话，通过迭代数据识别和分析，探索了 Lexis 图的实用性。研究人员在会议之间根据对话制作和调整可视化，以确保临床相关性，从而产生三种相关类型的可视化。结果领域专家根据诊断组和数据可视化确定了不同的用途。主要结果包括通过近乎实时的可视化提供及时反馈，支持对趋势的讨论和理解以及假设的建立。护理过程复杂性的可视化有助于对特定护理的评估。 个人和群体水平的组合可视化增加了患者的注意力，也可能起到激励从业人员和管理人员的作用。结论 Lexis 图可以帮助理解生存数据，引发护理人员之间的重要对话，支持护理质量改进和新观点，因此可以补充质量改进中的生存曲线。