Process Mining of Nursing Routine Data: Cool, but also Useful?

Background: Process mining is a promising field of data analytics that is yet to be applied broadly in healthcare. It can streamline the care process, leading to a higher quality of care, increased patient safety and lower costs.

Objectives: To get deeper insights into the emergence and detectability of delirium in a gerontopsychiatric setting.

Methods: We use process mining to create process models from routinely collected, anonymised nursing data from two gerontopsychiatric wards. We analyse these models to get a longitudinal view of the care processes.

Results: The process models comprise all activities during patients' stays but are too extensive and challenging to interpret due to the wide variation in care paths. Although the models give insight into frequent paths and activities, they are insufficient to explain the emergence of delirium meaningfully. No apparent difference between stays with or without delirium could be detected.

Conclusion: Conducting process mining on routinely collected data is easy, but the interpretation of the results was a challenge. We identified four limitations associated with using this data and gave recommendations on adapting it for further analysis.