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Track: Healthcare Data Analytics
Course: Understanding Healthcare Data Analytics 8 hours, 8 AMA PRA Category 1 Credits™
Description: This course is designed to provide people working in the healthcare industry (and those closely tied to the work of healthcare) a strong, practical understanding of data analytics. Learners will gain an understanding the importance of healthcare data analytics and how to apply their knowledge of analytics to every-day activities. The course will take learners through a number of data analytics exercises which represent real-life healthcare scenarios and show how to use Microsoft Excel to work with data. In addition, the course explains best practices in displaying data so that it is useful to various end-users. Finally, the course provides an overview of how analytics plays an important role in risk adjustment and predictive modeling, two of the foundations of value-driven care.
- Describe different types of data generated in health care
- Describe best practices for communication of data analysis results
- Identify limitations and challenges of re-using clinical data
- Use Microsoft Excel as a tool for data analytics, and demonstrate the ability to
- Describe reasons why data needs to be cleaned or modified before analysis
- Identify and correct basic errors in data
- Perform descriptive statistics
- Use pivot tables
- Describe the relationship between a database in a health IT system and data analysis tools
- Conduct a data re-use analyses for healthcare quality measurement utilizing a sample data set
Course: Clinical Data Analytics and the Learning Health System 9.5 hours, 9.5 AMA PRA Category 1 Credits™
Description: This course is designed to provide healthcare professionals who have a grounding in healthcare analytics with insight into the clinical context and use of data, best practices and advanced concepts in healthcare data analytics. The course includes practical exercises which represent real-life healthcare scenarios. It covers important privacy concerns, and current topics of interest, including machine learning, natural language processing, learning health systems, and usability.
There is no required prerequisite, but learners are strongly advised to complete Understanding Healthcare Data Analytics first.
- Describe the current state of data analytics in clinical settings, particularly the role that data analytics plays in value-based payment systems
- Identify key tools and approaches to improve analytics capabilities in clinical settings.
- Describe different governance and operations strategies in analytics in clinical settings.
- Analyze data used in population management and value-based care systems
- Describe ethical considerations in risk adjustment and population management