Mapping out care delivery with an assist from GIS.

© 2011 by Health Forum Inc. All rights resevered. Permission granted for digital use only. bership are actually getting effective care. Also, we now know that some health care decisions should be informed by knowledge of an individual’s race or ethnicity. By knowing the member’s address, which we know from the day he or she joins the program, we can harness census data that inform us about the racial and ethnic composition of the member’s community. Combining those data with the member’s surname, we can generate a probability distribution for that member’s race and ethnicity. Now, that probability distribution cannot be used at the individual level. In other words, you can’t really say anything about the race or the ethnicity of the individual based on this computation. But when you look at aggregates of numbers—for instance, if we look at 1,000 individuals and go through this process— we get a reasonable estimate for the racial and ethnic composition of that group. Using GIS to fill in this data gap enables us to stratify our own quality measures and those we report nationally, so we can begin to understand whether there are significant disparities across the racial and ethnic groups within our membership. If we discover such disparities, we can dig deeper and begin to address those, and find out if they are related to the educational materials or the ways in which we’re communicating with members. It allows us a much more comprehensive look at our quality measurements and potential disparities with race and ethnicity.