Normalization of Data

The transition into digitized data storage and access systems in health care requires a general adjustment in data capturing techniques. In the days of paper records, redundancy was a standard-information was written and rewritten with each patient visit. Digitized data storage and the normalization of data can help reduce this redundancy. Ideally, a patient can enter a facility and information previously submitted is readily available. The provider can simply apply updates and make adjustments to the information as necessary. This saves the health care provider time and the organization's information system space. Additionally, this decrease of data input can lead to improved quality and patient safety as fewer diagnostic errors associated with navigating a patient's information are less likely to occur.

Consider the following scenario:

You have recently been hired by a small community hospital as a nurse informaticist. One of your first responsibilities is to help to convert records to an electronic format. You decide to address the process of ordering medicine for patients and you need to develop a database to address the issue. Currently all of the patient information is contained in an Excel spreadsheet, which contains the following categories: PATIENT ORDER, PATIENT medical record number (MRN), PATIENT Name, Order Number, MEDICATION Name, MEDICATION Description, Quantity, PATIENT Address, and Date Ordered. In designing your database, you need to normalize the data in order to remove redundancies and duplications. What approach will you take to normalize the data?

Note: A PATIENT can have multiple orders but an order can be for only one MEDICATION. Patient medical record number (MRN) and Order Number are the primary keys.

To prepare:

• Review the information in your course text, Coronel, C. & Morris, S. (2015). Database systems: Design, implementation, and management (11th ed.). Stamford, CT: Cengage Learning, on how to normalize data.

• Refer to the scenario above and determine how you would normalize the information (there is not just one correct way of doing this).

• What form of normalization would use (1NF, 2NF, 3NF)?
• Illustrate the normalized form for PATIENT ORDER.
• Reflect on the issues you encountered as you attempted to normalize the list.
• Consider the consequences of not normalizing data.

Post by tomorrow 7/12/16 550 words in APA format with 3 references

1) A brief description of how you would normalize the data from the scenario and your rationale.
2) Describe the challenges that you encountered in determining how to normalize the data.
3) Explain the possible consequences of failing to normalize data.

Required Resources

Readings

• Coronel, C. & Morris, S. (2015). Database systems: Design, implementation, and management (11th ed.). Stamford, CT: Cengage Learning.

• Chapter 6, "Normalization of Database Tables" (pp. 190-232)

When designing a database, normalization can diminish the risk of redundant input. This chapter defines and describes how to perform normalization of a dataset.

• Chute, C. G., Beck, S. A., Fisk, T. B., & Mohr, D. N. (2010). The Enterprise Data Trust at Mayo Clinic: A semantically integrated warehouse of biomedical data. Journal of American Medical Informatics Association, 17(2), 131-135.

Retrieved from the Walden Library databases.

This article explores the Mayo Clinic's data warehousing system: the Enterprise Data Trust (EDT). The authors explain the various components of the EDT and detail how it can enhance research productivity, quality improvement, and best-practice monitoring.

• Westra, B. L., Subramanian, A., Hart, C. M., Matney, S. A., Wilsom, P. S., Huff, S., Huber, D. L., & Delaney, C. W. (2010). Achieving "meaningful use" of electronic health records through the integration of the nursing management minimum data set. The Journal of Nursing Administration, 40(7/8), 336-343.

Retrieved from the Walden Library databases.

This article describes the process of updating Nursing Management Minimum Data Sets (NMMDS) to achieve meaningful use compliance. The authors explain the methods used to update three NMMDS data elements, in addition to the results of the updates.

• Khan, R., & Saber, M. (2010). Design of a hospital-based database system: A case study of BIRDEM. International Journal on Computer Science and Engineering, 2(8), 2616-2621. Retrieved fromhttp://www.enggjournals.com/ijcse/doc/IJCSE10-02-08-050.pdf

The authors present the events at BIRDEM hospital surrounding the introduction of a more technologically-based system. The article shows the adjustments made by the staff to move from partial to fully digitized data storage and exchange. It also covers the process by which the data was transferred and organized within the new system.