Data Mining for the Health System Pharmacist

Abstract

Data mining is an application of computer technology that allows pharmacists to analyze the large amounts of clinical and administrative data related to patient care that health care organizations accumulate. Data mining is the process of taking the raw material (data) from the data warehouse and converting it to information that can be used by decision makers. Online analytical processing (OLAP) is the tool that accomplishes the data mining process.

This article introduces the concept of data mining and related technologies, including the data warehouse, data mart, and OLAP — concepts that are closely related to one another. The benefits of applying data mining to pharmacy practice, with a focus on the point of care, are discussed.

Successful data mining often requires an enterprise-wide information system as a prerequisite. Data can then be collected electronically at all points in the health care supply chain. A strategic view of the information flow in an acute care setting is provided. To further illustrate the application of data mining, several examples of OLAP integration are given. In addition, some unique data sources and a pharmacy specific reporting technology are described.

Keywords

data mining data warehouse online analytical processing

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References

Zupan

, Lavrac

, Keravonou

Data mining techniques and applications in medicine.

Artifi Intell Med. 1999; 16: 1–2.

Lubel

K.S.

Data mining: A new way to find answers. http://nile.ed.umuc.edu/~jmeinke/inss690/lubel.htm. Accessed September 6, 2000.

Shaw

M.J.

, Subramaniam

, Tan

G.W.

Knowledge management and data mining for marketing.

Decision Support Systems. 2001; 31: 127–37.

Inmon

W.H.

Building the Data Warehouse. 2nd ed. New York: John Wiley & Sons; 1996.

Edelstein

H.A.

Introduction to Data Mining and Knowledge Discovery. 3rd ed. Two Crows Corporation; 1999.

University of British Columbia – Computer Science. Data Mining: Introduction. http://www.cs.ubc.ca/nest/dbsl/mining.html.

Ebidia

, Mulder

, Tripp

, Morgan

M.W.

Getting data out of the electronic patient record: Critical steps in building a data warehouse for decision support.

Proc AMIA Symp. 1999; 745–9.

Hristovski

, Rogac

, Markota

Using Data Warehousing and OLAP in Public Health Care. http://www.amia.org/pubs/symposia/D200369.pdf.

Baldwin

Data Mining Comes to Health care: A Way to Excavate New Revenues and Savings. http://www.hcfinance.com/dec/dectech.htm

10.

Goodall

C.R.

Data mining of massive database in healthcare.

Journal of Computational and Graphical Statistics. 1999,8(3): 620–34.

11.

Milley

Healthcare and data mining: Using data for clinical, customer service and financial results.

Health Management Technology. http://www.healthmgttech.com/. Accessed October 15, 2002.

12.

Hamilton

D.B.

Data mining and health care.

Ober Kaler. http://www.ober.com/pubs/health/97sf30.htm. Accessed February 12, 2001.