Perform data mining activities on two Excel data sets. Prepare a 5 page report of findings, including whether data sets accurately depict performance, the use of data sampling methods in strategic decision making, and conclusions and recommendations about improving patient service and staff performance. Include in the report the analysis of the raw data in Excel data analysis tables.

Data Set 1: Clincial Performance

Data Set 2: Nursing Performance

Instructions

As a Vila Health data analyst, you have been asked to work on a project related to customer satisfaction and nursing staff performance. You will analyze two datasets. One concerns clinic performance, specifically, patient wait times and office visit lengths. The other dataset is on nursing performance.

After analyzing these two datasets, you will compose a report for the clinic's physicians based on your analysis.

Dataset 1: Clinic Performance

This first dataset contains raw data about clinic performance from a customer-service perspective. First, organize and analyze the raw data in Excel data analysis tables. You will include these tables in your report. Write your report about Dataset 1. Be sure to include these headings and address the bullets following each heading:

• Accurate Depiction of Clinic Performance.
  • Explain whether the sample can accurately depict clinic performance, noting variations and patterns.
• Data Sampling Methods and Strategic Decision Making.
  • Describe how to use data sampling methods in strategic decision making.
• Conclusions and Recommendations About Clinic Physicians and Customer Service.
  • Draw conclusions about clinic physicians and customer service.
  • Make two recommendations for improving patient service based on your analysis.

Dataset 2: Nursing Staff Performance

Dataset 2 provides information on nursing staff performance on two tasks. The data show a decrease in nursing staff productivity at one Vila Health clinic in the past few months. Use the Nursing Data Worksheet and the Pivot Table Report, both contained in Dataset 2 Nursing Performance 2016, to perform data mining techniques to determine how nursing staff performed when completing Tasks 1 and 2. Organize and analyze the raw data in Excel data analysis tables. You will include these tables in your report.

Note: Be careful of filters. Be sure to check data from various years.

Write your report, including all of the following:

Data Mining Techniques to Evaluate Nursing Staff Performance on Tasks.

• Explain how each of these data mining techniques can be used to evaluate nursing staff task performance:
  • Genetic algorithms.
  • Neural networks.
  • Predictive modeling.
  • Rule induction.
  • Decision trees.
  • K-Nearest neighbor.
• Include examples of the use of each data mining technique in relation to the nursing data.

Data Mining and Strategic Decision Making.

• Describe the use of data mining in strategic decision making.

Conclusions and Recommendations About Nursing Staff Performance.

• Draw conclusions about nursing performance on tasks.
• Create two recommendations for improving nursing performance.

Conclusion

Summarize the findings of your analysis of the two datasets. Draw conclusions about how the information from the datasets might be connected. For example, how might physician performance impact nursing tasks? Or what is the association between customer satisfaction and nursing task performance?

Resources: Data Collection

• • Horton, L. A. (2017). Calculating and reporting healthcare statistics (5th Rev. ed.). Chicago, IL: AHIMA Press. Available from the bookstore.
    • Chapter 12, "Basic Research Principles," pages 283–307.
    • Chapter 13, "Inferential Statistics in Healthcare," pages 309–320.
  • Cleary, M., Horsfall, J., & Hayter, M. (2014). Data collection and sampling in qualitative research: Does size matter? Journal of Advanced Nursing, 70(3), 473–475.
  • Kandola, D., Banner, D., O’Keefe-McCarthy, S., & Jassal, D. (2014). Sampling methods in cardiovascular nursing research: An overview. Canadian Journal of Cardiovascular Nursing, 24(3), 15–18.
  • Roberts, P. (2015). Data sampling for the right reasons. Business Intelligence Journal, 20(1), 33–38.

  Resources: Data Mining

  • PRINT

  • • Horton, L. A. (2017). Calculating and reporting healthcare statistics (5th Rev. ed.). Chicago, IL: AHIMA Press. Available from the bookstore.
      • Chapter 14, "Data Analytics," pages 321–330.
    • Lismont, J., Janssens, A., Odnoletkova, I., Vanden Broucke, S., Caron, F., & Vanthienen, J. (2016). A guide for the application of analytics on healthcare processes: A dynamic view on patient pathways. Computers in Biology and Medicine, 77, 125–134.
    • Ullah, Z., Fayaz, M., & Iqbal, A. (2016). Critical analysis of data mining techniques on medical data. International Journal of Modern Education and Computer Science, 8(2), 42–48.
    • Data Mining | Transcript.
      • In under five minutes, this Capella media piece presents some basic assumptions about leading a data mining project.

      Resources: Data Presentation

      • PRINT

      • • Horton, L. A. (2017). Calculating and reporting healthcare statistics (5th Rev. ed.). Chicago, IL: AHIMA Press. Available from the bookstore.
          • Chapter 11, "Presentation of Data," pages 245–281.
        • Ghazisaeidi, M., Safdari, R., Torabi, M., Mirzaee, M., Farzi, J., & Goodini, A. (2015). Development of performance dashboards in healthcare sector: Key practical issues. Acta Informatica Medica, 23(5), 317–321.