Geographic Information Systems and Data for Nurses

Geographic Information System for Health and Human Services

The GIS lessons on the healthcare costs are concerned with the analysis of the patterns the geographic distribution of Medicare costs; apart from that, they are aimed at data displaying, classification and analysis skills building (Environmental Systems Research Institute, Inc, 2016c) The topic of Medicare costs is significant for me as a practitioner and a citizen since it helps me to gain insights into the facts of our healthcare. I work in this context, and I am not unlikely to participate in its future changes and improvements. As for the data analysis skills, they are important for me as a professional who is likely to come into contact with health informatics.

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GIS map uses hospital referral regions (HRR) to mark per capita healthcare expenditures regionally (Environmental Systems Research Institute, Inc., 2016a, 2016b). This lesson provided me with insights into the geographical disparities in healthcare investments in the USA. Also, it offered me the experience of working with a spatial patterns map and hot spot analysis. As for informatics, I have learned the following three lessons.

First, I learned the differences between natural breaks, equal interval, and quantile classification. As a result, I see that different spatial patterns can be produced even when the data is the same, which indicates that “low” and “high” expenditures are rather relative terms. Secondly, although I had known that statistical significance requires more than the figures of spending, I received confirmations and actual experience of making differences in data meaningful, indicative of a disparity. Thirdly, the Environmental Systems Research Institute, Inc. (2016a) specifically points out that the presented analysis only shows the facts of disparities and does not explain them. For explanation, more data needs to be reviewed, which demonstrates the specifics and difficulties of making conclusions based on healthcare data.

Also, I would like to point out that such health informatics tools facilitate the use and analysis of data, which makes their development completely worthwhile.

Geographical findings from the Dartmouth Atlas of Health Care

To create the new database, I used the downloadable data from the Dartmouth Atlas of Health Care: Downloads (2016). It needed changes to be exported into Access, so I chose the dataset which reflected Medicare spending by state (to complement HRR) and year and Medicare enrollees.

There was no data for Medicare enrollees available for the year 2014. The tables represent the data for all the states for a particular year; I think that for this kind of database, this is the logical way of normalizing the tables with respect to 1NF to avoid repeating groups of data (Alexander & Kusleika, 2016). I created one-to-one relationships for all the tables within this database; I also think that it is possible to introduce a parent table for them.

The data suggests a number of geographical findings. My first finding is that the data appears to indicate that the funding is not always adequate when the number of enrollees is considered. Secondly, the data from the Atlas testifies to the existence of geographical healthcare disparities in the US. My lessons from GIS show that this data may be insufficient for these two conclusions, but the analysis of the hot and cold spots indicates that they are correct.

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Also, as was stated by the Environmental Systems Research Institute, Inc. (2016a, 2016c), it is apparent that such differences in money allocation demonstrate two key tendencies: underfunding and overfunding (which, naturally, leads to inefficient money spending). Therefore, thirdly, it can be concluded that the presented data is very likely to indicate the inefficiency in spending, which leads to the famous (or notorious) US healthcare expenditures that are the greatest in the world (World Bank, 2016).

However, the same data can be used to examine the disparities; if it is complemented by other data, more conclusions can be drawn, and they may be used to investigate the situation and look for solutions. As a result, I think that this data demonstrates the need for lobbying initiatives that attract greater attention to geographical healthcare disparities and promote their examination that is likely to explain possible solutions.


Alexander, M. & Kusleika, D. (2016). Access 2016 Bible. New York, NY: John Wiley & Sons.

Dartmouth Atlas of Health Care: Downloads. (2016). Web.

Environmental Systems Research Institute, Inc. (2016a). Analyze Medicare cost hot spots. Web.

Environmental Systems Research Institute, Inc. (2016b). Map Medicare costs. Web.

Environmental Systems Research Institute, Inc. (2016c). Where Does Healthcare Cost the Most? Overview. Web.

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Geographic Information Systems and Data for Nurses
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World Bank. (2016). Health expenditure. Web.

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