Healthc Inform Res.  2012 Sep;18(3):178-185. 10.4258/hir.2012.18.3.178.

Data Mining Application in Customer Relationship Management for Hospital Inpatients

• Lee EW

• KMID: 2229468
• DOI: http://doi.org/10.4258/hir.2012.18.3.178

Abstract

OBJECTIVES
This study aims to discover patients loyal to a hospital and model their medical service usage patterns. Consequently, this study proposes a data mining application in customer relationship management (CRM) for hospital inpatients.
METHODS
A recency, frequency, monetary (RFM) model has been applied toward 14,072 patients discharged from a university hospital. Cluster analysis was conducted to segment customers, and it modeled the patterns of the loyal customers' medical services usage via a decision tree.
RESULTS
Patients were divided into two groups according to the variables of the RFM model and the group which had significantly high frequency of medical use and expenses was defined as loyal customers, a target market. As a result of the decision tree, the predictable factors of the loyal clients were; length of stay, certainty of selectable treatment, surgery, number of accompanying treatments, kind of patient room, and department from which they were discharged. Particularly, this research showed that when a patient within the internal medicine department who did not have surgery stayed for more than 13.5 days, their probability of being a classified as a loyal customer was 70.0%.
CONCLUSIONS
To discover a hospital's loyal patients and model their medical usage patterns, the application of data-mining has been suggested. This paper suggests practical use of combining segmentation, targeting, positioning (STP) strategy and the RFM model with data-mining in CRM.

Keyword

Hospital-Patient Relations; Patient-Centered Care; Patient Satisfaction; Data Mining; Decision Tree

MeSH Terms

Data Mining
Decision Trees
Hospital-Patient Relations
Humans
Inpatients
Internal Medicine
Length of Stay
Patient Satisfaction
Patient-Centered Care
Patients' Rooms

Figure

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  Figure 1 Model of patterns of loyal customers' medical service use.

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  Figure 2 Comparison between decision tree and logistic regression by receiver operating characteristic curve.

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