Healthcare systems and practices

Health Care

"Decision Tree Analysis." (2010). MindTools. Cited in: http://www.mindtools.com/index.html

Matthews, C. (April 28, 2003). "Decision Making With Decision Trees." Inc. Cited in:

http://www.inc.com/articles/2003/04/25423.html

It seems that there are a number of ways in which disaparate data can be analyzed and then synthesized into a more reasonable format. Graphically, the decision tree seems to be one of the easiest and most effective. A decision tree is a support tool that uses models of decisions in picture or graphic form to help model possible consequences based on decisions made along the way. For instance, in Project 1, if decision a is made, then B. And C. might be consequences; but if decision S. is made, there would be a whole new set of actions and reactions. The tree gives the user a way to more appropriately sketch "what ifs" without necessarily resorting to statistical analysis. It can also spur discussion within group projects and help managers find alternatives. It also seems that the trees are viable because even with little hard (verified) data, the user is able to generate better ideas that describe situations that may occur within a product. It is also easier to change variable using this type of tool ("Decision Tree Analysis," 2010).

Student #1 accurately describes the article in question that focuses on using a decision tree when looking at possibilities of rolling out a new pharmaceutical product. Shown were the facts, the breakeven point, and potential alternatives. I was not able to understand from the post whether they agreed with the methodology or technique; but am assuming they were in agreement based on their reading of the analysis. This shows, too, how quickly a business committee can take financial data and place other variables (e.g. what is the probability of success) and then change situations to show what could happen if only certain variable were changed.

Asolva. (April 29, 2010). "Data Mining Techniology." Newswise. Cited in:

http://www.newswise.com/articles/data-mining-technology-used-in-business-now-applied-to-hospital-information-systems-for-infection-control-programs

Hand, D., et.al. (2001), Principles of Data Mining, MIT Press.

Kimball, R. And M. Ross, (2002), the Data Warehouse Toolkit, John Wiley & Sons.

The term "data mining" refers to the process of taking a large amount of data, sorting through it, and refining appropriate and relevant information. Typically, it is used with ERP systems to help make strategic decisions based on the patterns that come out of the data review. It is also that more formal and statistical analysis of large sets of data within complex ERP Systems. Data warehousing is a term meaning the storage of information for an organization. The data warehouse is the place that the data is stored and organized. Data warehousing, per se, is passive; while data mining is active. One needs to have the data in a depository of some kind before it can be mined for use. Both data mining and data warehousing are essential to the effective management of the strategic planning process. The amount and quality of the data warehoused is directly related to the organization's ability to use that data appropriately. Mining data from various sources increases the statistical validity of the data set, and provides more useful information quicker.