Course description
Data Analytics and Visualization in Health Care
Big data is transforming the health care industry relative to improving quality of care and reducing costs--key objectives for most organizations. Employers are desperately searching for professionals who have the ability to extract, analyze, and interpret data from patient health records, insurance claims, financial records, and more to tell a compelling and actionable story using health care data analytics.
The course begins with a study of key components of the U.S. health care system as they relate to data and analytics. While we will be looking through a U.S. lens, the topics will be familiar to global learners, who will be invited to compare/contrast with their country's system.
With that essential industry context, we'll explore the role of health informatics and health information technology in evidence-based medicine, population health, clinical process improvement, and consumer health.
Using that as a foundation, we'll outline the components of a successful data analytics program in health care, establishing a "virtuous cycle" of data quality and standardization required for clinical improvement and innovation.
The course culminates in a study of how visualizations harness data to tell a powerful, actionable story. We'll build an awareness of visualization tools and their features, as well as gain familiarity with various analytic tools.
Who should attend?
Prerequisites:
This course is ideal for those who have completed a bachelor's degree. Some experience in the health care field recommended, but not required. Fundamental knowledge of statistics and research methods preferred.
Training content
Introduction to Health Care
- Components of Health Care
- Stakeholders
- Care Settings
- Financing
- Public Health
- Regulatory/Research
- Challenges and Opportunities
- The Triple Aim
- Quality and Cos
- Patient Experience/Access
- Systems Approach
- Evidence-Based Medicine
- Quality Improvement
- Value-Based ReimbursementHealth Care Trends
- Demographics/Population Health
- Consumerism/Personalized Medicine
- Emerging Trends in Health Care
Introduction to Health Informatics
- Overview of Health IT
- What is Health Informatics?
- How Health Informatics Supports Triple Aim
- Health IT Systems and Components
- EMR/EHR Modules and Ancillary Data Systems
- Enterprise Systems vs. Best of Breed
- Structured Versus Unstructured Data
- EHR Adoption
- EHR Regulations
- Barriers to EHR Adoption
- Interoperability and HIT Standards
- Health IT Standards
- Data Exchange
- Clinical Decision Support
- HIPAA Security
- Public Health IT and Consumer Engagement
Introduction to Data Analytics
- Data Terms and Concepts
- Why Data Analytics?
- Virtuous Cycle in Analytics
- Data Terminology
- Big Data Terminology
- Getting Data Ready for Analysis
- Considerations Before Analyzing
- Integrating Data Across Data Sets
- Data Governance, Privacy, and Security
- Data Governance Within the Organization
- Patient Identification
- Regulatory Considerations and Data Security
- Analysis with Artificial Intelligence
- Machine Learning in Health Care
- Natural Language Processing in Health Care
- Making Data Usable to Others
- Finalizing Data for Analysis
- Communicating Data
Introduction to Visualizations
- Value of Visualization
- Visualization Best Practices
- What Not to Do
- Types Based on Use Case
- Visualizations of Complex Data
- Dashboard Design
- Analyzing Visuals
- Exploratory vs. Explanatory Visualization
- Quantitative vs. Qualitative Visualization
- Uses in Health Care
- Tools for Analysis and Visualization
- Gartner Software Benchmarking
- Current Tools
Course delivery details
This course is offered through Rochester Institute of Technology, a partner institute of EdX.
8-10 hours per week
Costs
- Verified Track -$249
- Audit Track - Free
Certification / Credits
What you'll learn
- Identify current forces disrupting today's health care industry
- Summarize current health care trends and their impact on cost, quality, and patient engagement
- Describe health informatics' role in clinical workflow and patient engagement
- Identify components of health information technology
- Explain the importance interoperability in health care analytics
- Summarize data collection, processing, and analysis best practices
- Explore the implications of artificial intelligence on extraction and analysis of complex data sets
- Interpret data analysis results from a visualization example
- Identify visualization best practices
- Prepare a simple data visualization using health care data
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