Health Care Analytics

(1.2 CEUs)


Because of advances in information technology and reporting requirements, most healthcare organizations have access to large sets of structured and unstructured data. New uses for these data have the potential to improve quality of care, reduce costs, and enhance operational efficiency. However, many healthcare professionals are short on the skills needed to use these data to their potential. This course will cover these skills. Attendees will learn how to discover patterns and meanings from large sets of data – collectively, referred to as data analytics. Using a hands-on approach, the course will expose attendees to a variety of tools, frameworks, and methods that will enhance their skills. All course activities will be in the healthcare context.

To ensure that attendees have a common understanding of the context framing the course, the course provides a high-level viewpoint on healthcare data. Accordingly, there will be discussions on the drivers for the proliferation of healthcare data. The course will also describe challenges associated with harnessing such data. In addition, it will assess how healthcare organizations can innovate around these data to derive competitive advantage.

As a bonus, because data visualization is critical to the understanding and adoption of data-driven solutions, this course will also introduce participants to the use of different types of data visualization techniques. It will also involve discussions on strategies for choosing the best form of visualizations for healthcare data and provide instructions on how to weave compelling narratives around the visualizations.

Participants will:

  1. Learn about fundamental concepts in data analytics, and how factors such as technology democratization and quality improvement initiatives have transformed the Healthcare Analytics landscape
  2. Learn how healthcare organizations can innovate around large data and derive competitive advantage from Healthcare Analytics
  3. Learn of practical frameworks, tools, and techniques for framing business problems and/or opportunities correctly, and analyzing large datasets in healthcare
  4. Learn how to use TIBCO Spotfire as at tool for analyzing large data sets, and for presenting insights from data analytics projects in a compelling and interesting way

This course is designed for anyone who wants to learn about how to use data to address business problems and/or opportunities in the healthcare sector (both clinical and non-clinical functions). The course is also for healthcare professionals that are curious about the strengths and limitations of various insights obtained from data-driven models, and how these may be integrated with expert opinions. The course does not require advanced IT or programming skills. However, proficiency in Excel, and familiarity with basic statistical concepts would be helpful.

The course will meet from 9 a.m. to 5 p.m. each day with a 1-hour lunch break each day.

Participants will receive bound copies of the lecture notes, as well as resources for supplemental reading on the various analytics techniques that will be covered in class.

The course will use TIBCO Spotfire for the hands-on exercises. No previous experience with TIBCO Spotfire is assumed. However, to participate in the hands-on exercises, participants are strongly encouraged to bring a laptop computer with Spotfire installed. A free 30-day trial of TIBCO Spotfire is available here.


Norman A. Johnson | Bio
Onyi Nwafor | Bio


Day 1
Time Discussion Topics

7-8 a.m.


8-8:15 a.m.

Course Introduction

8:15-8:40 a.m.

Connections with Health Care

8:40-9:20 a.m.

Fundamentals of Data Science and Analytics - What these terms are and the context in which they apply for best use



9:30-10 a.m.

Core Data Concepts - such as Data Storage, Data Exchange, Machine Learning, and the Internet of Things (IoT) in Health Care

10-10:15 a.m.

The Analytical Mindset - The analytical approach to problem solving in a structured way

10:15-10:25 a.m.

Overview of key methods and their applications - what you can do and what you cannot do by analytical methods 

10:25-10:50 a.m.

Descriptive Analytics - The use of query, reporting tools and technologies to summarize data for decision making and support

10:50-11 a.m.


11-11:40 a.m.

Predictive Analytics - The use of data, algorithms, and other techniques to predict or determine outcomes you expect

11:40 a.m.-12 p.m.

Diagnostic Analytics - The use of methods to examine data and identify specific reasons why outcomes occurred

12-1 p.m.


1-1:20 p.m.

Prescriptive Analytics - The use of methods to compare various options and deciding on criteria for defining "best" choices on a relative

1:20-1:35 p.m.

Simulation - How to replicate events in the business context and identify possible causes of past outcomes, and analyze the behaviors of

1:35-1:45 p.m.

Other Prediction Analytics Methods -  Exploring different paths to outcomes through decision analysis

1:45-3:35 p.m.

Other Prediction Analytics Methods -  Exploring different paths to outcomes through decision analysis

3:35-3:45 p.m.

Class discussion and exercises: Applying concepts

3:45-4:20 p.m.

Split into groups and work on exercises

4:20-4:30 p.m.


4:30-5 p.m.

Road mapping - How it all comes together

Day 2
Time Discussion Topics

8 a.m.-12 p.m.

Group exercises and Road mapping


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CONTACT US: Gary Randazzo, Director:, 713-743-4754 | Jennifer Coppock, Program Coordinator: 713-743-4702