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Diabetic Data Warehouses

The National Academy of Sciences convened in 1995 for a conference on massive data sets. The presentation on health care noted that “massive applies in several dimensions . . . the data themselves are massive, both in terms of the number of observations and also in terms of the variables . . . there are tens of thousands of indicator variables coded for each patient” (Goodall, 1995, paragraph 18). We multiply this by the number of patients in the United States, which is hundreds of millions. Diabetic registries have existed for decades. Datamining techniques have recently been applied to them in an attempt to predict diabetes development or high-risk cases, to find new ways to improve outcomes, and to detect provider outliers in quality of care or in billing services (Breault, 2001; He, Koesmarno, Van, & Huang, 2000; Hsu, Lee, Liu, & Ling, 2000; Kakarlapudi, Sawyer, & Staecker, 2003; Stepaniuk, 1999; Tafeit, Moller, Sudi, & Reibnegger, 2000). Diabetes is a major health problem. The long history of diabetic registries makes it a realistic and valuable target for data mining.