Exploring the usefulness of Lexis diagrams for quality improvement

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.

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.

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.

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.