Introduction Data mining is an analytical process used to extract data for the purpose of providing information. In this assignment, you will perform data mining activities and apply the results to different uses in healthcare information settings. Preparation Complete the following: View the Presentation of Data video. Complete the Data Mining Introductory Practice activity. For this assignment, you will work with the following data sets: Clinic Performance Data Set [XLSX]. Nursing Data Set [XLSX]. Instructions As a data analyst for St. Anthony Medical Center, you have been asked to work on two projects related to customer satisfaction and nursing staff performance. You will analyze two data sets and compose a report for St. Anthony’s board of directors based on your analysis. Clinic Performance The Clinic Performance Data Set [XLSX] contains raw data about performance at your clinic from a customer service perspective. Use this data set to complete the following: Organize raw data. Create charts to show your organized data. Analyze data samples. Select data elements to compare. Calculate averages. For example: What is the overall average wait time? What is the average wait time for each physician? What is the overall average visit time? What is the average visit time for each physician? What is the average payment? Perform data mining activities. Compare data elements. For example: How does the average wait time compare to the average visit time? How does the average visit time compare to the average payment? Draw conclusions about clinic physicians and customer service. Describe data sampling methods and the use of data sampling in strategic decision making. Identify at least two different types of data sampling methods. Describe how to use data sampling methods in strategic decision making. Explain if the sample can provide an accurate depiction of clinic performance, noting variations and patterns. Create two recommendations for improving patient service based on the results of your analysis. Nursing Data Nursing Data Set [XLSX] provides information on nursing staff performance on two tasks. The data show that there has been a decrease in productivity for the nursing staff at one of the St. Anthony clinics in the past few months. Organize raw data. Create charts to show your organized data Analyze data samples. Select data elements to compare. Calculate averages. For example: What percentage of RN’s completed tasks? What percentage of LPN’s completed tasks? What is the percentage of tasks completed by units? Perform data mining activities. Compare data elements. Select one of the data mining techniques and perform data mining to determine how the nursing staff performed when completing Task 1 and Task 2. Identify the technique selected. Describe how the technique was used. What patterns/trends were identified used in technique? Discuss data mining tools and the use of data mining in strategic decision making. Provide a brief summary of data mining techniques that can be used to evaluate the nursing staff tasks. Discuss data mining tools. Include a description of how each technique can be used to help determine or detect in healthcare, including an example of the use of each data mining technique in relation to the nursing data. Genetic algorithms. Neural networks. Predictive modeling. Rule induction. Fuzzy logic. Decision trees. K-nearest neighbor. Additional Requirements Your assignment should also meet the following requirements: Written communication: Written communication should be clear and generally free of grammatical errors. Format: Word document, including data analysis tables from Excel. APA formatting: Use current APA style and format for the paper, references, and citations. See the Writing CenterLinks to an external site. for APA resources specific to your degree level. Length of paper: Five pages. Font and font size: Times New Roman, 12 point. Competencies Measured By successfully completing this assignment, you will demonstrate your proficiency in the following course competencies and rubric criteria: Competency 1: Interpret healthcare data. Organize raw data. Competency 3: Use data analysis tools and privacy concepts to support health information integrity and data quality. Analyze data samples. Perform data mining activities. Discuss data mining tools and the use of data mining in strategic decision making. Competency 4: Apply statistical strategies to analyze healthcare data. Describe data sampling methods and the use of data mining in strategic decision making. Competency 5: Communicate in a professional manner to support healthcare data analytics. Create a document that is clearly written and generally free of grammatical errors. Follow APA style and formatting guidelines for references and citations.