• Length:
    8 Weeks
  • Effort:
    8–10 hours per week
  • Price:

    Add a Verified Certificate for $249 USD

  • Institution
  • Subject:
  • Level:
  • Language:
  • Video Transcript:
  • Course Type:
    Self-paced on your time


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.

About this course

Skip About this course

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.

What you'll learn

Skip 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

Module 1: Introduction to Health Care

Components of Health Care
Care Settings
Public Health

Challenges and Opportunities
The Triple Aim
Quality and Cos
Patient Experience/Access

Systems Approach
Evidence-Based Medicine
Quality Improvement
Value-Based Reimbursement

Health Care Trends
Demographics/Population Health
Consumerism/Personalized Medicine
Emerging Trends in Health Care

Module 2: 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

Module 3: 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

Module 4: 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

Meet your instructors

Travis Masonis
Adjunct Professor
Rochester Institute of Technology
Matthew Phillips
Adjunct Professor
Rochester Institute of Technology
Jodi Lubba
Adjunct Professor
Rochester Institute of Technology
Johnny Brown
Adjunct Professor
Rochester Institute of Technology

Pursue a Verified Certificate to highlight the knowledge and skills you gain
$249 USD

View a PDF of a sample edX certificate
  • Official and Verified

    Receive an instructor-signed certificate with the institution's logo to verify your achievement and increase your job prospects

  • Easily Shareable

    Add the certificate to your CV or resume, or post it directly on LinkedIn

  • Proven Motivator

    Give yourself an additional incentive to complete the course

  • Support our Mission

    edX, a non-profit, relies on verified certificates to help fund free education for everyone globally

Who can take this course?

Unfortunately, learners from one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.