Introduction to Health Care Data Analytics

According to a 2011 McKinsey Report, there will be a shortage of talent necessary for organizations to take advantage of big data. By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions. ¹

And when the consulting firm Accenture surveyed its clients on their big-data strategies in April 2014, more than 90 percent said they planned to hire more employees with expertise in data science—most within a year. However, 41 percent of the more than 1,000 respondents cited a lack of talent as a chief obstacle. ²

Data analytics plays an increasingly greater role in health care organizations, while many healthcare professionals lack training in this vital area. Introduction to Health Care Data Analytics, a 10 week, 25-hour online course addresses this pressing need. It provides healthcare employees in a broad range of roles (clinical and non-clinical) with foundational knowledge of analytics, covering key components of the data analysis process, including strategies for effectively capturing and communicating information gleaned from data analytics efforts. The overarching goal is to equip participants to effectively contribute to and/or conduct data analytics projects. This introductory course does not have an IT-intensive focus; the only technical skill required is basic proficiency with Excel. Familiarity with statistical thinking would be helpful, but is not required.

• Describe the tools and techniques used for data analytics in health care organizations.
• Discuss the role of data analytics in quality and performance improvement efforts.

• 100% online, self-paced course available from July 31, 2017 - October 7, 2017
• Program includes a mix of interactive activities, videos, lectures, reading and optional online discussion

• There are no pre-requisites for this course; however, basic knowledge and skill in the use of Microsoft Excel is highly recommended.
• Appropriate for anyone interested in healthcare data analytics.

MODULE 1: INTRODUCTION TO QUALITY IMPROVEMENT AND DATA ANALYTICS

1. Discuss the drivers for health care transformation
2. Identify quality initiatives that have shaped the national health care landscape
3. Define health care quality and value
4. Describe the background and evolution of quality and performance improvement
5. Discuss the QI frameworks that utilize analytics
6. Define health care data analytics
7. Discuss how analytics can help transform health care

MODULE 2: HEALTHCARE DATA AS AN ORGANIZATIONAL ASSET

1. Describe the data information, knowledge and wisdom hierarchy
2. Explain how data can be an organizational asset
3. List sources of health care data
4. Describe the challenges HCO’s face when using data for quality and performance improvement
5. Describe an organizational approach for effective use of data analytics
6. Describe the role of data governance
7. Identify attributes of quality data (Ref: AHIMA)
8. Identify features of a data driven organization

MODULE 3: WORKING WITH DATA

1. Describe the information value chain
2. Discuss the importance of data context and relevance to business processes
3. Define common data types  
4. Define basic statistical terms
5. Identify basic methods of summarizing data
6. Identify common errors in working with data

MODULE 4: DATA ANALYTICS TOOLS AND TECHNIQUES

1. Define data analytics terms
2. Describe the role of the data analyst
3. Describe the process steps of data analytics and the tools used in each step
4. Apply appropriate tools and techniques to analyze and interpret healthcare data effectively
5. Describe enterprise data architecture as seen in health care organizations
6. Describe the various types of databases and how they are structured
7. Describe key data warehouse concepts

MODULE 5: USING DATA TO SOLVE PROBLEMS

1. Identify types of problems encountered in health care
2. Describe the purpose and use of  Key Performance Indicators (KPI’s) to drive organizational performance
3. Review problem-solving methodologies and tools
4. Apply the DMAIC problem-solving model to a health care issue
5. Integrate relevant Project Management methodologies into data analytics projects
6. Apply statistical thinking in the analysis of data
7. Identify the role of advanced tools for problem-solving with data

MODULE 6: USING DATA TO TELL THE STORY

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

• Video presentations by analytics experts
• Interactive learning activities
• Recorded voice-over PowerPoint lectures
• Required readings
• Optional discussion forums  
• Case study