INTRO TO NURSE AS A DATA ANALYST *Beta

Important: This course is not eligible for a refund, after access to content has been granted.  However, a full refund can be issued, if the course purchase was determined by the course provider to be an error.  A deducted administrative fee of $25.00 USD will be applied. 

Instructor: Greetings, I am an educational consultant.  I hold a master’s degree in nursing education, and I am also a Certified Risk Adjustment Coder.  I wrote this course to help bridge the gap between Nursing, Data Analytics, and Risk Adjustment. I have been an RN for over 12 years, and still find the nursing profession fascinating.

Course Syllabus

Course:  INTRODUCTION TO NURSE AS DATA ANALYST *Beta: Nursing, Data Analytics, and Risk-adjustment 

Course Description 

This 96-hours (8 hours per week for a duration of 12 weeks) beginner’s level course provides foundational skills and knowledge in the nurse’s role as an analyst that will prepare the student to contribute more effectively to their organization’s data analytics and performance improvement efforts. Students can request an additional 30 days of access to the course, as permitted by the course instructor. We offer a convenient online format which allows you to complete weekly assignments on your own schedule.  There are no pre-requisites, and we will teach you basic skills in spreadsheets to get you started. 

Copyright 

Copyright © Advanced Clinical Documentation for Nurses (ACD4N) 2024. Any illegal reproduction such as copy, share, or print of this content will result in immediate legal action.

Technology Requirement 

• Course was designed for desktop view. We do not recommend viewing course content via mobile phone.
• Sending and receiving emails 
• Sending and receiving attachments via email 
• Using a web browser 
• Downloadable workbook/spreadsheets, and other related documents 

Hardware 

• PC, Mac 
• Speakers 
• Microphone 
• Zoom (virtual meetings)

Systems 

• Windows 10 or newer 
• Mac OS 10.14 or newer 
• Microsoft Office 2019 or newer 
• Internet connection- High speed internet  
• Download speed of at least 25mbps 
• Upload speed of at least 3mbps 

Course Goals & Objectives  

1. Discuss the role of the data analyst in quality and performance improvement efforts in the healthcare industry.  
2. Describe the tools and techniques used by nurses for data analytics in health care. 
3. Explore 4 types of data analytics: Descriptive, Diagnostic, Predictive, and Prescriptive
4. Brief concept review of the following: ICD-10-CM codes, Risk-Adjustment methodology, and role and responsibility of professional medical coder.
5. Identify techniques to communicate insights gained from data analysis to key stakeholders.
6. Build your professional portfolio that can be presented to prospective employers, or when seeking opportunities for advancement within your current organization. 

Course Pre-requisites  

• There are no pre-requisites for this course; however basic computer skills, and familiarity with Microsoft Excel or Google Analytics is highly recommended. 
• Course is tailored for nurses, however open to anyone interested in learning about the Nurse Data Analyst role. 

Course Completion 

• Downloadable projects to build your professional portfolio 
• Students who successfully complete the course will receive a certificate of completion  

Introduction to the role of the Nurse Data Analyst  

1. Discuss the drivers for nursing profession transformation
2. Identify quality initiatives that have shaped the national health care landscape
3. Define health care quality 
4. Discuss the quality improvement frameworks that utilize analytics
5. Define health care data analytics
6. Discuss how analytics can help transform health care

Working with Data 

1. Discuss the importance of data context and relevance to business processes
2. Define common data types 
3. Recognize common patterns
4. Describe use of calculations in spreadsheets
5. Identify common graphical representations of data including pivot tables, bar charts and piecharts 

Data Analytics Tools and Techniques 

1. Define data analytics terms
2. Describe the process steps of data analysis and the strategies used in each step
3. Identify tools and techniques used to analyze and interpret healthcare data effectively
4. Describe the various types of data sources and how they can be used

Using the Data to Tell the Story  

1. Describe ways to effectively display data 
2. Select appropriate options for displaying information
3. Identify background information that should be included in reports
4. Determine what information stakeholders want and need to know
5. Determine the best ways to communicate information to specific stakeholders

Access to supplemental resources and recommended reading 

Projects

• Three exploratory data analysis projects
• Final project: Data-driven Proposal 

Course Format 

• This program includes a PowerPoints, videos, lectures and online discussions as well as individual activities hosted via a learning management system called LearnPress. The course website is Courses – ACD4N, and accessible with any PC or MAC with Internet access. 
• Course work is primarily asynchronous (self-paced). Periodic synchronous (live, real- time activity) instructor-led workshops may be included.  
• Students will be required to access the online classroom to review the lectures and complete activities, including PowerPoints, quizzes, questionnaires and online discussion forums. Downloadable dataset. 

Course Evaluation 

• There are two ways that the course will be evaluated: 
• Immediately after the course you will be asked to complete a course survey. 
• Between 30-60 days after the course ends, you may be asked to complete a post-course survey. 

Academic Honesty 

It is expected that students will complete the ‘Test Your Knowledge’ knowledge checks, projects and other assignments as outlined in the lesson in order to demonstrate that they have learned the course material. The LearnPress learning management system tracks student activity including visits to each page, quiz attempts, activity completion to validate course completion. Responses to assignments, quizzes and exams will be based on the student’s own work It is expected that students will properly cite any references or works that are not their own and honor any intellectual property rights including copyrights and trademarks.  

For questions about how to use APA citation for citing academic literature, please refer to https://owl.purdue.edu/owl 

Technical Support 

For technical issues such as problems with registration, access to course or LearnPress issues, report in the Contact page at www.acd4n.com/Contact.  

Course Summary

© Advanced Clinical Documentation for Nurses (ACD4N) 2024. Any illegal reproduction, such as share, copy, or print of this content will result in immediate legal action.