NURS 6411 Information to Knowledge Data Mining and Warehousing Week 9 Essay Assignment Paper

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NURS 6411 Information to Knowledge Data Mining and Warehousing Week 9 Essay Assignment Paper

NURS 6411: Information and Knowledge Management | Week 9

How can data mining and data warehousing help your health care organization?

Consider the example of Charlotte, who is an informatics analyst working for a multi-state chain of hospitals. The executives to whom she reports have asked her to identify how much time it takes for the different insurance companies with whom they do business to make payments on claims. She has been instructed to pay close attention to claims processed between February 2010 and March 2011. In order to address this task efficiently, Charlotte uses her organization’s data warehousing system, which maintains a centralized source of copies of claims, regardless of the originating hospital. After accessing the database, Charlotte applies data mining techniques to generate new correlations between payment speed, insurance companies, and other variables unique to that time period. Due to her organization’s data mining and warehousing capabilities, Charlotte was able to easily identify the amount of time it took for insurance companies to make payments, as well as speculate on the cause of delays.

Charlotte’s case is just one illustration of the benefits that data warehousing and data mining present to health care organizations.

This week, you examine the relationship between data warehousing and data mining and how they can be applied to benefit health care organizations.

Learning Objectives

Students will:

  • Contrast guided data mining with automated data mining
  • Assess how health care data should be warehoused to allow for data mining
  • Formulate strategies for addressing concerns about of data mining

Learning Resources

Note: To access this week’s required library resources, please click on the link to the Course Readings List, found in the Course Materials section of your Syllabus.

Required Readings

Coronel, C. & Morris, S. (2017). Database systems: Design, implementation, and management (12th ed.). Boston, MA: Cengage Learning.

  • Chapter 13, “Business Intelligence and Data Warehouses” (pp. 589-636)This chapter explores data warehousing and how it improves organizational decision making. It also evaluates how, in some situations, the internet may affect data storage and assessments.

Kristianson, K. J., Ljunggren, H., & Gustafsson, L. L. (2009). Data extraction from a semi-structured electronic medical record system for outpatients: A model to facilitate the access and use of data for quality control and research. Health Informatics Journal, 15(4), 305–319.

In this article, the authors demonstrate the importance of structuring diagnostic data for optimum data extraction and patient care. In addition, they evaluate the efficiency of data management standards in electronic medical records (EMRs).

Kulkarni, M. (2010). A case-based data warehousing courseware. 2010 IEEE International Conference on Information Reuse and Integration (IRI), 245–248.

This article evaluates how beginning designers can learn and implement key concepts of data warehousing. The method highlighted here is hands on and involves the creation of a warehouse tailored to suit a specific data set.

Jukic, N., & Nicholas, J. (2010). A framework for collecting and defining requirements for data warehousing projects. Journal of Computing & Information Technology, 18(4), 377–384.

This article proposes a database framework that is standardized to suit various data processing applications. The authors highlight the planning steps for data warehouses and explore methods for creating a database framework that will suit the needs of the end-users.

Hey, T. (2010). The big idea: The next scientific revolution. Harvard Business Review, 88(11), 56–63.

The author of this article explains how applying machine learning in data analysis can produce scientific discoveries and accurate predictions. The article describes several successful applications of machine learning across the domains of health care, oceanography, business, and more.

McAfee, A. (2011). What every CEO needs to know about the cloud. Harvard Business Review, 89(11), 124–132. Retrieved from


This article highlights the benefits that cloud computing provides for all business organizations. The author addresses the transition into the widespread use of cloud technology while debunking common criticisms about its usability and security.

Required Media

Laureate Education, Inc. (Executive Producer). (2012). Data Mining and Data Warehousing. Baltimore, MD: Author.

This multimedia piece describes data warehousing and data mining. It highlights their interrelationship and role in the storage and access of data in databases.

Note: The approximate length of this media piece is 5 minutes. Please click on the following link for the transcript: Transcript(PDF).

As discussed in this week’s readings, data warehousing is a method of data storage that allows for streamlined data management and retrieval. Data mining software aids in clarifying the relationships between stored data and assists in retrieving specific information as needed. In health care organizations, the information this process yields can be used to cut costs and improve patient care.

For this Discussion, you explore the concept of data mining from a health care perspective.

To prepare:

  • What are the potential benefits of using data mining in health care?
  • Review the information in the Learning Resources on the different types of data warehousing and how the one selected impacts data mining.
  • Review the Hey article, “The Next Scientific Revolution.” Consider how data mining through machine learning can be applied to health care.
  • Read the section on data mining on pp. 671-673 in the course text, Database Systems: Design, Implementation, and Management and consider how it connects to the content in the Hey article. According to the text, are the data mining techniques Hey describes guided or automated?
  • Using the Walden Library, locate at least one specific example of each type of data mining (guided and automated) in health care. The examples you identify should be different from the examples discussed in the Hey article.
  • Reflect on your initial impressions of automated data mining in health care. What are your thoughts on applying this type of data mining to patient care? Consider possible drawbacks of both guided and automated data mining. What approaches and strategies could be used to address those concerns?
  • Consider any ethical ramifications of using data mining or machine learning as a tool for prognosis.

By Day 3

Post an analysis of how data mining can be beneficial to a health care system. Assess how the type of data warehousing used can impact the ability to mine data. Describe examples of the successful use of guided data mining and automated data mining within health care and cite your source. Describe any reservations you have or ethical issues you foresee in using data mining to provide health care information. What approaches and strategies could be used to address those concerns? Justify your responses.

Read a selection of your colleagues’ responses.

By Day 6

Respond to at least two of your colleagues on two different days. Provide additional insights you have on the benefits and drawbacks of using data mining in health care. In addition, outline an approach or strategy that could be used to address the reservations about data mining that your colleagues described.

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