Using data mining to segment healthcare markets from patients' preference perspectives

Area of Science:

• Health Informatics
• Data Science in Healthcare
• Market Segmentation Analysis

Background:

• Healthcare customer experience management is an emerging field.
• Data mining applications are increasing across industries.
• Limited application of data mining in healthcare customer experience.

Purpose of the Study:

• To demonstrate data mining techniques for patient segmentation.
• To identify patient segments based on healthcare preferences and demographics.
• To compare data mining with traditional clustering methods.

Main Methods:

• Utilized in-patient care data from a health network (2006).
• Employed data mining and hierarchical clustering (average linkage, Pearson correlation).
• Compared clustering procedures for determining segmentation variables.

Main Results:

• Data mining identified three distinct patient segments via cluster analysis.
• These segments exhibited significantly different demographic profiles.
• Data mining proved efficient and effective for market segmentation.

Conclusions:

• Data mining offers an efficient alternative to traditional statistical methods for market segmentation.
• Factor analysis is recommended for reducing variables when dealing with numerous cluster variables.
• Data mining enhances understanding of patient healthcare preferences in customer experience management.