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

    FREE
    Add a Verified Certificate for $199 USD

  • Institution
  • Subject:
  • Level:
    Advanced
  • Language:
    English
  • Video Transcript:
    English

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.

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

  • 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
       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 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

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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.