Research On Data Mining Application In HCRM

Throat-cutting competition in the medical market has made customers the most important resources to increasing hospitals. With many years of development, Hospital Information Systems (HIS) has not only created perfect economic and social benefit for the hospital, but also accumulated a great deal of relevant data, which will certainly become precious resources for the hospital to establish and develop customer relationship management. How to use the resources effectively and provide some meaningful advices for the customer relationship management of the hospital is the concern of hospital and the relevant, which is also the purpose of this research.This paper analyzes the domestic and international application of customer relationship management and data mining. Based on the related fields of literature review, the research is mainly carried out in following aspects: First, this paper explored the basic concept and methods of customer relationship management and data mining, selecting the methods and tools suitable for this study. Second, preliminary design of Hospital Customer Relationship Management (HCRM) system, combined with the function of HCRM system, the specific applications and methods of data mining involved in HCRM is discussed. Again, a classification model about patient treatment frequency and a forecast model about customer response were constructed through the analysis of patient information, treatment information, activity response information and other content by the Clementine mining tool. Finally, according to the results of the analysis on the model and the model prediction, study the behavioral characteristic of each type of customer and their contribution to the hospital, then the hospital could provide personalized services.In a word, as computer technology continues to progress, the accumulation of customer information, the effect of data mining will be further displayed. While the hospital is a customer-oriented industry, therefore, data mining will certainly become a powerful tool to the hospital customer relationship management. This paper made a research into the customer classification and customer response prediction, which strives to give some implication for hospital customer relationship management.