Health Care Analytics

January 4, 2018 | 8 a.m. - 5 p.m.

January 5, 2018 | 8 a.m. - 12 p.m.

(1.6 CEU)

 

COURSE OBJECTIVE
By helping you to understand what data science and analytics are, this course will show you how to respond to the proliferation of data in health care through innovation. The course will also help you to understand the roles of core data technologies and tools that are resources for creating value in the health care industry. By doing this course, you will:

  1. Learn about fundamental concepts in data science and analytics;
  2. Learn how data proliferation, technology democratization, and the need to improve both clinical and operational performances have brought Health Care Analytics to the forefront of managers’ attention;
  3. Engage in discussions on how health care organizations can innovate around large data and derive competitive advantage from health care analytics;
  4. Learn how to apply analytics across a wide range of health care activities

COST
$2,130

INSTRUCTORS
Norman A. Johnson | Bio
Onyi Nwafor | Bio

TENTATIVE AGENDA

Day 1
Time Discussion Topics

7-8 a.m.

Breakfast

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

9:20-9:30

Break

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.

Break

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.

Lunch

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.

Break

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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