Data Analytics and Visualization in Health Care (edX)

Data Analytics and Visualization in Health Care (edX)
Course Auditing
Categories
Effort
Certification
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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.
Misc

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Data Analytics and Visualization in Health Care (edX)
Learn best practices in data analytics, informatics, and visualization to gain literacy in data-driven, strategic imperatives that affect all facets of 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.

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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


Syllabus


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



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Course Auditing
216.00 EUR
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.

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.