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

Julian Jonk1,2, Michael Schaller1, Michael Netzer1

  • 1UMIT TIROL - Private University for Health Sciences, Medical Informatics and Technology, Institute of Medical Informatics, Hall in Tirol, Austria.

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|May 20, 2022
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Summary

Process mining in gerontopsychiatry shows care process variations but struggles to detect delirium. Routine data analysis is feasible, but interpretation requires refinement for clinical insights.

Keywords:
Data MiningDeliriumDelivery of Health CareProcess Assessment

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Area of Science:

  • Healthcare Data Analytics
  • Process Mining Applications

Background:

  • Process mining, a data analytics technique, has potential in healthcare for improving care quality, patient safety, and cost-efficiency.
  • Its application in gerontopsychiatric settings remains underexplored.

Purpose of the Study:

  • To investigate the emergence and detectability of delirium using process mining in a gerontopsychiatric environment.
  • To gain longitudinal insights into patient care pathways.

Main Methods:

  • Utilized process mining to construct models from anonymized nursing data from two gerontopsychiatric wards.
  • Analyzed process models to understand the longitudinal progression of care activities.

Main Results:

  • Process models encompassed all patient stay activities but were complex and difficult to interpret due to diverse care paths.
  • Frequent care paths and activities were identified, but delirium emergence could not be meaningfully explained.
  • No significant differences were detected between patient stays with or without delirium.

Conclusions:

  • Process mining of routine data is straightforward, but result interpretation poses challenges.
  • Identified four limitations in using routinely collected data for this analysis.
  • Provided recommendations for data adaptation to enhance future process mining studies in healthcare.