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Area of Science:

  • Health Informatics
  • Data Science
  • Medical Data Analysis

Background:

  • Healthcare databases contain vast amounts of data offering significant potential for analysis.
  • Understanding patient treatment pathways is crucial for improving healthcare outcomes.
  • Existing methods may lack the granularity to capture the full complexity of sequential treatment events.

Purpose of the Study:

  • To present a novel multilevel analysis methodology for generating and analyzing sequential healthcare treatment events.
  • To enable the automatic generation of event sequences at various abstraction levels from source data.
  • To demonstrate the applicability of the methodology through a case study.

Main Methods:

  • Development of a multilevel analysis framework for sequential healthcare data.
  • Automatic generation of patient treatment event sequences at different levels of detail.
  • Application of the methodology to a cohort of colorectal cancer patients.

Main Results:

  • Successfully generated and analyzed sequential treatment events at multiple abstraction levels.
  • Identified distinct patterns and variations in treatment pathways within the colorectal cancer cohort.
  • Demonstrated the capability of the methodology to provide nuanced insights into patient care.

Conclusions:

  • The proposed multilevel analysis methodology effectively captures the complexity of sequential healthcare treatments.
  • This approach facilitates a deeper understanding of patient journeys and treatment variations.
  • The methodology holds promise for advancing data-driven healthcare analytics and personalized medicine.