International Journal of Computer
Trends and Technology

Research Article | Open Access | Download PDF

Volume 56 | Number 1 | Year 2018 | Article Id. IJCTT-V56P104 | DOI : https://doi.org/10.14445/22312803/IJCTT-V56P104

Analysis and Application of Data Mining in CRM Systems of Healthcare Insurance

Ms. Sheetal Macwan, Mr. Samrat Khanna

Citation :

Ms. Sheetal Macwan, Mr. Samrat Khanna, "Analysis and Application of Data Mining in CRM Systems of Healthcare Insurance," International Journal of Computer Trends and Technology (IJCTT), vol. 56, no. 1, pp. 27-31, 2018. Crossref, https://doi.org/10.14445/22312803/IJCTT-V56P104

Abstract

Data mining techniques in Customer Relationship Management (CRM) has a very important role in the practice; it is an important means of gaining and maintaining customer information, and improving customer value. We have determined to compare a variety of techniques, approaches and different tools and its effect on the healthcare field. Data mining created new concept with customer relationship management where different companies can gain a reasonable advantage. The goal of data mining is to turn data into facts, figures, or text which can be processed by a computer into knowledge or information. The main reason of data mining application in healthcare systems is to create a mechanical tool for make out and distributes relevant healthcare information. The aim of this paper is to make a detailed study report of diverse types of data mining applications in the healthcare sector and to minimize the difficulty of healthcare data transactions. A relative study of different data mining applications, techniques and different methods applied for takeout knowledge from database produced in the healthcare industry.

Keywords

Data Mining, healthcare system, healthcare industry, Customer Relationship Management (CRM).

References

[1] Kincade, K. (1998). Data mining: digging for healthcare gold. Insurance & Technology, 23(2), IM2-IM7
[2] Milley, A. (2000). Healthcare and data mining. Health Management Technology, 21(8), p. 44-47
[3] Christy, T. (1997). Analytical tools help health firms fight fraud. Insurance & Technology, 22(3), p. 22-26.
[4] Newton’s Telecom Dictionary, Harry Newton, CMP Books, http://www.cmpbooks.com
[5] IDC & Cap Gemini. Four elements of customer relationship management. Cap Gemini White Paper.
[6] M. FreemanThe 2 customer lifecycles Intelligent Enterprise, 2 (16) (1999), p. 9
[7] Pilot Software. An introduction to data mining: discovering hidden value in your data warehouse. http://www3.shore.net/~kht/text/dmwhite/dmwhite.htm
[8] Information Discovery, Inc. A characterization of data mining technologies and processes: an Information Discovery, Inc. White Paper.
[9] Hand, D.J. (1998). Data mining: statistics and more? The American Statistician, 52(2), p. 112-118.
[10] Edelstein H. Data mining: exploiting the hidden trends in your data. DB2 Online Magazine. http://www.db2mag.com/9701edel.htm
[11] Milley, A. (2000). Healthcare and data mining. Health Management Technology, 21(8), p. 44-47
[12] Christy, T. (1997). Analytical tools help health firms fight fraud. Insurance & Technology, 22(3), p. 22-26.
[13] Oakley, S. (1999). Data mining, distributed networks and the laboratory. Health Management Technology, 20(5), p. 26-31.
[14] Friedman, N.L. & Pliskin, N. (2002). Demonstrating value-added utilization of existing databases for organizational decision-support. Information Resources Management Journal, 15(4), p. 1-15.
[15] McQueen, G. & Thorley, S. (1999). Mining fool’s gold. Financial Analysts Journal, 55(2), p. 61-72.
[16] Cody, W.F. Kreulen, J.T. Krishna, V. & Spangler, W.S. (2002). The integration of business intelligence and knowledge management. IBM Systems Journal, 41(4), p. 697-713.