Data Mining Application in Customer Relationship Management for Hospital Inpatients.
10.4258/hir.2012.18.3.178
- Author:
Eun Whan LEE
1
Author Information
1. Department of Social and Preventive Medicine, Sungkyunkwan University School of Medicine, Suwon, Korea. ewlee@skku.edu
- Publication Type:Original Article
- Keywords:
Hospital-Patient Relations;
Patient-Centered Care;
Patient Satisfaction;
Data Mining;
Decision Tree
- MeSH:
Data Mining;
Decision Trees;
Hospital-Patient Relations;
Humans;
Inpatients;
Internal Medicine;
Length of Stay;
Patient Satisfaction;
Patient-Centered Care;
Patients' Rooms
- From:Healthcare Informatics Research
2012;18(3):178-185
- CountryRepublic of Korea
- Language:English
-
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.
