Exploring the usefulness of Lexis diagrams for quality improvement.

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.

Relationship between patient costs and patient pathways.

PURPOSE: The purpose of this paper is to identify actual (as-is) patient pathway variation among breast cancer patients and to investigate the relationship between pathways and the cost incurred by patients. DESIGN/METHODOLOGY/APPROACH: Both quantitative and qualitative methods were employed to analyze data from four Swedish hospital groups. Quantitative methods include event-log data mining and statistical analyses on the related patient cost from the Swedish breast cancer quality registry and case-costing system. Qualitative methods included collaboration with and interviewing domain experts. FINDINGS: Unique pathways, followed by only one patient, were generally costlier than the most and less frequent pathways. Earlier study findings are confirmed for mastectomy patients, with more frequent pathways having a lower cost, whereas contradicting and inconclusive results emerged for the partial mastectomy patient groups. Highest variation in pathways was identified for patients receiving chemotherapy. PRACTICAL IMPLICATIONS: The common belief - if one follows a standardized patient pathway, then the cost will be lower - should be re-examined based on the actual pathways that occur in reality. ORIGINALITY/VALUE: The relationships between patient pathways and patient cost allow more complex insights, beyond the general causal relationship between successfully implementing a "to-be" care pathway and lower cost. This highlights data-driven research's importance, where actual pathways (as-is) provide more useful information than to-be care pathways.

Process Mining for Quality Improvement: Propositions for Practice and Research.

OBJECTIVE: Process mining offers ways to discover patient flow, check how actual processes conform to a standard, and use data to enhance or improve processes. Process mining has been used in health care for about a decade, however, with limited focus on quality improvement. Hence, the aim of the article is to present how process mining can be used to support quality improvement, thereby bridging the gap between process mining and quality improvement. METHOD: We have analyzed current literature to perform a comparison between process mining and process mapping. RESULT: To better understand how process mining can be used for quality improvement we provide 2 examples. We have noted 4 limitations that must be overcome, which have been formulated as propositions for practice. We have also formulated 3 propositions for future research. CONCLUSION: In summary, although process mapping is still valuable in quality improvement, we suggest increased focus on process mining. Process mining adds to quality improvement by providing a better understanding of processes in terms of uncovering (un)wanted variations as to obtain better system results.