Predictive Analytics in
Health Care (MSA 8750-E)
Syllabus
Instructor:
Email:
Office:
Address:
Phone:
Office Hrs:
Dr. Abhay Nath Mishra
amishra@gsu.edu (the best and quickest way to reach me)
Room No. 809, Robinson College of Business
Institute of Health Administration, 35 Broad Street, Suite 805, Atlanta GA 30303.
(404) 413-7638
By appointment
Class Schedule: TBD
Day of the week; time; classroom location
Prerequisites:
Three core courses in analytics: MSA 8000, MSA 8050 and MSA 8200
Course Description
The health care industry is one of the largest producers of raw data in the United States.
Advances in information gathering methods, increasing standardization and the widespread use
of information technologies among health care providers, payers and consumers are further
fueling the size and variety of datasets collected. Current regulations and business requirements
will continue to push health care organizations to collect and analyze even more data. With the
current availability and further creation of a large amount of raw data at different levels (e.g.,
patient, facility, hospital, health system, physician, disease condition, etc.) and of different
variety (structured and unstructured), health care organizations need tools that allow them to
effectively sift through these enormous datasets and extract actionable information and
knowledge to make smart businesses decisions. It is essential in this context to understand how
to model, using advanced analytical methods, complex business problems faced by the
healthcare industry and to solve these problems using available data. Furthermore, it is vitally
important to use advanced analytical techniques to make reasoned predictions about future
events and to take preemptive actions.
Predictive modeling is the process of developing models to better predict future outcomes for an
event of interest by exploring its relationships with explanatory variables from historical data. A
large number of methods with roots in statistics, informational retrieval and econometrics have
been developed to extract knowledge from large data sets. These methods can be applied
successfully in diverse areas, such as health care market basket analysis, churn analysis for
hospitals and insurance companies, health insurance fraud detection, readmission assessment,
personalization of treatment regimen, patient risk management and performance-based payment
analysis. The course introduces the techniques of predictive modeling and analytics in a data‐rich
health care business environment. It covers the process of formulating business objectives, data
selection, preparation, and partition to successfully design, build, evaluate and implement
predictive models for a variety of health care applications. Predictive modeling tools such as
classification and decision trees, neural networks, regressions, association analysis, cluster
analysis, etc. will be discussed in detail and applied to practical health care problems.
The focus of this course will be on the rigor of the analytical techniques, as also their
implementation. Students will be expected to have a background in statistical and quantitative
approaches. We will also spend time on the interpretation of results, but the focus will clearly be
on the application of tools and techniques. In other words, the course will focus more on the
creation and less on the consumption of analytics.
Course Objectives:
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Use retrieval and manipulation tools for health information extraction and reporting
Describe different methods of predictive analytics and their application in health care
Rigorously apply analytics techniques on healthcare problems
Be proficient with analytics software for health care data preparation and analysis
Apply predictive analytics tools to make better operational, financial and clinical
decisions
Create analytics for the consumption of top-level managers
Course Materials:
Required text:
Data Mining for Business Intelligence, 2nd Edition, by Galit Shmueli, Nitin R. Patel, and
Peter C. Bruce (Wiley: 2010). (New copy comes with a valid license to XLMiner.)
ISBN-10: 0470526823; ISBN-13: 978-0470526828
Data-Driven Healthcare: How Analytics and BI are Transforming the Industry, 1st
Edition, by Laura B. Madsen (Wiles and SAS Business Series: 2014). ISBN10: 1118772210; ISBN-13: 978-1118772218
The texts will be supplemented with additional readings. Readings will be posted on
Desire2Learn. Students are also encouraged to follow interesting developments in health
analytics and report them in the class.
The instructor will provide the HCUP and AHA datasets on which the course will be based.
Course Conduct and Policies:
This course uses a blend of lecture/discussion and assignments. It is absolutely essential for you
to come fully prepared to the class. All assignments are due at the beginning of the class. We
will not take attendance, but we expect you to be present in every class. Absenteeism or lack of
preparation will adversely affect your grade. If you must miss a class, let us know as soon as
possible. As a sign of courtesy to us and your fellow classmates, please don’t browse the web,
check your email or facebook or twitter, or use your cell phone in any non-academic manner
while you are in the class. Finally, follow the policies related to academic integrity (more on
that later!).
Grading:
Your final grade in this class will depend on four components. The distribution is as follows:
Group Project
Group Assignments
Exams
(5)
(2)
30 % (group effort)
20 % (group effort)
50 % (individual effort)
Group Assignments
Students will be expected to complete 5 homework assignments over the course of the semester.
These assignments are designed to reinforce your understanding of the topics covered.
Assignments must be turned in at the beginning of or before the class period of the due date. No
late work is accepted. The instructor will create groups of 2-3 students. Each group is expected to
complete assignments independently. Assignments should be submitted via email at
amishra@gsu.edu to Professor Mishra. Additionally, you are required to bring a printed copy of
the assignment to the class.
Group Project
The purpose of the group project is to encourage students to apply (and expand on) their learning
in the class in an area that is of special interest to them. I strongly suggest you begin working on
this project from the first week and delegate tasks amongst your group members in an efficient
manner. The students will leverage the two datasets – HCUP and AHA – and provide data-driven
insights based on the tools we cover in the class. These analyses conducted and insights should
build on those we cover during the regular class. It is important that students be deeply immersed
in the two datasets mentioned above, and potentially other datasets, and learn how to combine
different datasets and analyze them using analytics tools.
Submit your paper via email at amishra@gsu.edu to Professor Mishra. Additionally, you are
required to bring a printed copy of the paper to the class.
Exam and Exam Policies:
There will be two exams. The exams will cover the materials discussed in the course.
1. You are not allowed to discuss the exams with anyone. You (and your collaborator if
he/she is a fellow student) will receive a score of 0 if any infraction is noticed and
established. In addition, other actions may also be taken.
2. Exam missed due to an excused absence must be made up within one week of returning
to class for full credit or no credit will be given. Exam missed due to an unexcused
absence may not be made up. Documentation proving the excused absence may be
required at the time the exam is made up.
3. The first exam will be held in the class. The second exam will be held during the exam
week. Submit your exam electronically at amishra@gsu.edu to Professor Mishra. Exams
will be based on datasets that students will have worked on before.
Academic Dishonesty Policy
Cheating on an examination or assignment, or assisting another student in cheating, is not
permissible. You may not discuss exam questions or case write-up issues with anyone. If you
have any questions, please see the instructor. Further, students are expected to abide by the
Georgia State University code.
On each exam or assignment you will be asked to write out and sign the following pledge.
"I pledge on my honor that I have not given or received any unauthorized assistance on this
exam/assignment."
Special Needs
If you have a disability and/or special needs, you should bring this to my attention as soon as
possible, but not later than the second week of class.
Course Feedback
Your constructive assessment of this course plays an indispensable role in shaping education at
Georgia State. Upon completing the course, please take the time to fill out the online course
evaluation.
Final Grades
Georgia State University has implemented a plus and minus grading method. The suggested cutoffs for various grades are:
 96-100 = A+; 93-95 = A; 90-92 = A-; 87-89 = B+; 83-86 = B
 80-82 = B-; 77-79 = C+; 73-76 = C; 70-72 = C-; 60-69 = D
 <60 = F
The instructor reserves the right to modify these cut-offs.
Tentative Class Schedule (adjustments may be necessary)
Date
Topic
Class 1
Introduction to Predictive Modeling in Competing on
Health Care, Software Setup
Analytics;
Chapters 1,2 from
SPB and Chapter
1 from M.
Health Care Data Extraction and Online reading
Manipulation, SQL, Relational data model material + notes
and RDBMS
Data Extraction and Manipulation of Online reading
Unstructured Health Care Data
material + notes
OLAP and Multidimensional Analysis
An Introduction
Data Quality
to OLAP
Summarization and Data cubes
Multidimensional
Terminology and
Technology;
Chapters 3 and 4
from M
Data Exploration, Visualization and Chapters 3, 4
Dimension Reduction
from SPB;
Chapter 7 from M
Association and Health Market-based Chapter 13 from
analysis
SPB;
Cluster Analysis
Chapter 14 from
SPB;
Classification and Predictive Modeling
Chapters 5, 9
from SPB;
Predictive Modeling using Regression
Chapters 6 and 10
from SPB;
Predictive Modeling using Naïve Bayes Chapters 8 and 9
and Regression Trees
from SPB;
Time Series and Smoothing
Chapters 15 and
17 from SPB;
Predictive Modeling of Disease Trends Chapter 11 from
using Neural Networks
SPB;
Text Mining in Health Care
Online reading
material + notes
Class 2
Class 3
Class 4
Class 5
Class 6
Class 7
Class 8
Class 9
Class 10
Class 11
Class 12
Class 13
Class 14
Reading
Assignment
Due
Assignment 1
Assignment 2
Assignment 3
Assignment 4
Assignment 5
Project Presentation
Project report due (3 days after the last class day)
Take-home Final Exam (during the final exam time)
All the best!!