BIOMEDICAL INFORMATICS PROGRAM
College of Osteopathic Medicine
Health Professions Division
Course Title:
Clinical Decision Support Systems
Course Number:
MI – 5204
Course Credits:
3
Course Director:
Jacob Krive, Ph.D., MBA, MS, CPHIMS, LSS Green Belt
Adjunct Assistant Professor
Phone: 954.262.1038
Email: jkrive@nova.edu
3200 South University Drive, Fort Lauderdale, FL 33328
Dates:
01/06/2014 – 04/20/2014
Days and Time:
Thursdays: 01/09, 01/23, 02/06, 02/20, 03/06, 03/20, 04/03, 04/17 from
7:00PM – 8:00PM, ET
Location:
Online within Blackboard
Course Description:
This course introduces students to theoretical, statistical, and practical concepts underlying modern
medical decision making. Students will be provided a review of the multiple methods of knowledge
generation for clinical decision support systems (CDSS) and create their own prototype of CDSS.
Current implementations of stand-alone and integrated CDSS will be evaluated. Techniques for planning,
management, and evaluation of CDSS implementations will be reviewed. Human factors, including workflow integration, and the ethical, legal and regulatory aspects of CDSS use will be explored, as applicable
to commercial implementations in patient care settings. Future models of healthcare, supported by CDSS
and evidence-based medicine, will be discussed and reviewed.
1/10
Course Textbooks (Required):
Berner, Eta S. (Ed.) Clinical Decision Support Systems (2nd Ed.). Springer, 2009. ISBN-10: 1441922237,
ISBN-13: 978-1441922236
Osheroff, Jerome A., Teich, Jonathan M., Levick, Donald, Saldana, Luis, Velasco, Ferdinand T., Sittig,
Dean F., Rogers, Kendall M., & Jenders, Robert A. (2012). Improving outcomes with clinical decision
support: An Implementer’s Guide (2nd Ed.). Chicago, HIMSS. ISBN: 978-0-9844577-3-1
Topol, Eric J. (2012). The creative destruction of medicine: How the digital revolution will create better
health care (1st Ed.). New York, Basic Books. ISBN-10: 0465025501, ISBN-13: 978-0465025503
Course Textbooks (Recommended):
Shortliffe, Edward H. and Cimino, James J. Biomedical Informatics: Computer Applications in
Health Care and Biomedicine, May 25, 2006). Springer; 3rd edition 2006. ISBN-10: 0387289860
ISBN-13: 978-0387289861
Greenes, Robert A. (Ed.) Clinical Decision Support: The Road Ahead (1st Ed.) Elsevier, 2007. ISBN10: 0123693772, ISBN-13: 978-0123693778
Course Software (no cost to student):
Exsys Corvid: http://www.exsys.com/
Course Structure and Requirements:
Students are required to be familiar with the course contents. The course material will be reinforced
through participation in bulletin-board discussions, written assignments, quizzes, programming projects,
and a team course capstone project presented via discussion groups in Power Point format and the last
live online session at the end of the course.
Course Goals:
After completion of the course, the student should be able to analyze clinical problems using one of
several knowledge-generation methods for CDSS; design a prototype of a successful CDSS; review the
history of CDSS development and implementation; assess the human-factors and regulatory issues of
CDSS systems; describe current CDSS software marketplace and industry implementations; understand
basic concepts of evidence-based medicine and the main building blocks linking evidence-based and
CDSS; and apply their knowledge to constructing basic CDSS components and logic. Students should
also be able to summarize and apply fundamental statistical concepts involved in medical decision
making.
Course Learning Objectives:
Upon completion of this course, the student will comprehend the following issues and objectives:
1. Describe the scope and kinds of clinical decision support systems; analyze CDSS effectiveness in
terms of implementing for diagnostic and therapeutic purposes.
2. Evaluate the linkage of CDSS to the basic concepts of evidence-based medicine.
2/10
3. Apply practice guidelines for clinical decision support, including commonly-used formalisms and
authoring tools for computer-interpretable guidelines.
4. Describe the social and political forces driving implementations of CDSS in the clinical field.
5. Compare and contrast the types of CDSS available in commercial and research implementations.
6. Apply statistical methods and logic concepts, such as probability, regression, Boolean logic, set
theory, and inference, to underlying medical decision making.
7. Evaluate at least three methods of knowledge generation for CDSS, including decision trees, neural
networks, and Bayesian analysis.
8. Compare the advantages and disadvantages of supervised vs. unsupervised learning methods in
data-mining applications.
9. Evaluate how CDSS fold into the overall hospital and/or medical office health information technology
environment.
10. Analyze technology and business characteristics of successful CDSS implementations using recent
industry cases as guidelines and input to build student’s own attributes of an effective CDSS
implementation.
11. Recognize business and clinical implementation and maintenance challenges in commercial CDSS
projects, as well as possible resolutions to these challenges.
12. Assess risks involved with poor CDSS implementations from the following standpoints: health
outcomes, quality of care, medical error rates, and patient and provider satisfaction standpoints.
13. Discuss ethical and regulatory issues involved in design and implementation of CDSS systems.
14. Identify opportunities for use of CDSS in personal health records and shared decision making.
15. Identify a basic clinical problem or an operational situation with the purpose of simulating an expert
system to assist clinicians with problem resolution process.
16. Present a full implementation of CDSS with commercially applicable attributes, aimed at solving
specific clinical problem or improving clinical workflow.
17. Integrate theoretical and practical knowledge of current and future CDSS learned in class, to apply in
healthcare settings.
Evaluation Format:
The following evaluation formats will be employed in this course: quizzes, written assignments, programming
projects, online discussions, and integrative CDSS capstone project.
Grading Policy:
This course adheres to the examination policy as stated in the COM student handbook. Student grades
will be based upon the level of performance in meeting course requirements. These requirements include
assignments, class participation in online sessions and discussion boards, and course project.
Task
Quiz #1
Quiz #2
Online Session Attendance (1st
and Last)
CDSS Research Paper
Evidence-Based Medicine
Paper
Medical Data Vocabulary
Paper: Part I
% of Final Grade
5%
5%
5%
10%
10%
5%
3/10
Medical Data Vocabulary
Paper: Part II (online
discussion)
Expert System Programming
Project
CDSS Programming Project
Comparative Case Analysis
Paper
CDSS Capstone Project
Total
5%
10%
10%
10%
25%
100%
Course Grading Scale:
0 – 100 Scale
94 - 100
90 - 93
87 - 89
84 - 86
80 - 83
77 - 79
73 - 76
70 - 72
Below 70
Letter
Grade
Scale
A
AB+
B
BC+
C
CF
Quality Points Scale
4.0
3.7
3.3
3.0
2.7
2.3
2.0
1.7
0.0
Course Online Sessions:
There will be 8 synchronous discussion sessions throughout the course occurring from 7:00 to 8:00 pm
(ET) on Thursday evenings on the weeks listed in the syllabus. A computer with audio in
(microphone) and out (speakers or headphones) and an internet connection, preferably
broadband speed, will be required to participate in the synchronous discussion sessions.
Course Attendance Policy:
Attendance of online sessions and scheduled bulletin board discussions is required by HPD policy. Nonattendance without prior approval from the instructor will be considered unexcused, and will be penalized
with a grade of 0% for the relevant sessions. Attendance of the first and last class meetings is required;
penalty above applies for failure to show up. All other sessions are not mandatory but crucial to your
success in this course, so attendance is highly encouraged and recommended.
There is a penalty for delayed submissions of assignments, assessed at 10% per day after the due date.
Assignments not submitted or presented within 5 days of the deadline in Blackboard, without prior
arrangement with the course director, will receive a grade of zero (0) for the assignment.
4/10
Course Assignments:
There will be 4 papers, 2 quizzes, 1 online board discussion, and 2 programming projects to be
submitted online by due date. Assignments will be graded within one week after due date. Descriptions
and grading criteria will be available in the Blackboard course area.
Course Final Project:
Each student will be required to create a decision support system prototype to assist with a specific
problem in medical decision making. Details of the project can be found in the assignments tab on the
course page.
Course Schedule:
This course starts on 01/06/2014 and ends on 04/20/2013, total of 14 weeks.
Week
Date
Topic(s)
Assignment(s)
Readings
01/06/2014
Overview and
Mathematical
Foundations of CDSS
Online Session #1
Thursday, 01/09
7-8PM
Berner, Chapters 1
and 2
01/13/2014
Evidence-Based
Medicine as
Foundation for CDSS
Evidence-Based Medicine
Paper
01/20/2014
Data Mining for CDSS;
Types of CDSS;
Design and
Implementation of
CDSS
1
2
3
4
01/27/2014
Diagnostic Decision
Support Systems
Topol, Chapter 2
Online Session #2
Thursday, 01/23
7-8PM
Readings in
Blackboard
Berner, Chapters 3
and 4
Quiz 1
Research of a Clinical
Decision Support System
Paper
Berner, Chapter 5
5
02/03/2014
Clinical Trials of
Information
Intervention
Online Session #3
Thursday, 02/06
7-8PM
Berner, Chapter 7
Review Exsys Corvid
Tutorials
5/10
6
02/10/2014
The Future of CDSS
and Related
Technologies:
Genomics, Genomic
Algorithms, and
Genome Sequencing
7
02/17/2014
8
02/24/2014
Ethical and Legal
Issues in Decision
Support
Conceptual
Foundations for
Commercial CDSS
Implementations
Download Exsys Corvid
Software
Expert System
Programming Exercise
Online Session #4
Thursday, 02/20
7-8PM
Topol, Chapters 5 and
6
Berner, Chapter 6
Start CDSS Programming
Exercise
Complete CDSS
Programming Exercise
Osheroff, Part I Intro
and Chapter 1 (pp. 1 41)
9
03/03/2014
Organizing a
Successful CDSS
Program
Online Session #5
Thursday, 03/06
7-8PM
Osheroff, Chapter 2
Vocabulary Coding System
Exercise: Part I
10
Osheroff, Chapter 3
03/10/2014
Key CDSS Building
Blocks
03/17/2014
Foundations for
Effective CDSS
Interventions
Vocabulary Coding System
Exercise: Part II
Topol, Chapters 7 and
8
11
Online Session #6
Thursday, 03/20
7-8PM
Osheroff, Part II Intro,
Chapters 5 and 6
(pp.129 – 212)
Quiz 2
03/24/2014
Spring Break
12
03/31/2014
CDSS Industry
Implementation: Case
Analysis
No assignments – enjoy
your time off
No reading
assignments – enjoy
your time off
Online Session #7
Thursday, 04/03
7-8PM
Berner, Chapters 8, 9,
and 10 (pick two cases
for reading and
analysis from Berner
or Osheroff texts, or
find your own cases)
Comparative Case
Analysis Paper
6/10
13
04/07/2014
Practical
Implementation of
Clinical Interventions
with CDSS
14
Start CDSS Capstone
Project
Osheroff, Chapters 7
and 8
Online Session #8
Thursday, 04/17
7-8:30PM
04/14/2014
The Future of CDSS:
Rebooting the Life
Sciences Industry
Complete and Submit
CDSS Capstone Project
Topol, Chapters 10
and 11
Present Projects During
Online Session #8
Course Topics:
1. Overview and Mathematical Foundations of CDSS
a. Describe the history of CDSS development.
b. Describe the type of CDSS available in commercial and research implementations.
c. Define and compare knowledge based and non knowledge based CDSS.
d. Define the four components/benefits of most CDSS.
e. Describe the dependencies and interactions between CDSS, EMR, and CPOE applications.
f. Understand limitations of CDSS, general implementation challenges, and political landscape
surrounding CDSS projects in healthcare organizations.
g. Define basic statistical concepts, including sensitivity, specificity, PPV, NPV, incidence, and
prevalence.
h. Apply Boolean logic to solving clinical workflow problems
i. Derive Bayes Rule from conditional and joint probabilities
j. Understand odds ratios
k. Learn how to choose and interpret diagnostic tests
l. List the steps involved in constructing a differential diagnosis
m. Define inference engine as one of the core knowledge-based methodologies
n. Conceptually understand neural networks and genetic algorithms as core examples of the
non knowledge based systems.
o. Understand basic purpose of genetic algorithms.
2. Evidence-Based Medicine as Foundation for CDSS
a. Understand basic concepts of evidence based medicine (EBM) and its impact on healthcare.
b. Understand operational, political, and philosophical issues surrounding application of
evidence based practice methods in the clinical settings
c. Review examples of evidence based knowledge, systems, and applications
d. Understand links and dependencies between CDSS and evidence based.
e. Become familiar with a 5-step EBM model.
f. Understand several types of clinical trials and analyze their impact on building, managing, and
using CDSS.
g. Conceptualize health outcomes research and analyze CDSS impact in this new field
h. Ask main questions assumed when constructing successful EBM.
7/10
Produce an EBM analysis paper, covering main concepts of EBM and students’ opinions on it
based on material learned in class.
Data Mining for CDSS, Types of CDSS, Design and Implementation of CDSS
a. Define knowledge discovery in databases.
b. Compare and understand data mining and machine learning.
c. Define supervised vs. unsupervised learning and appropriate situations for application of
these strategies
d. Become familiar with knowledge presentation formats.
e. Learn how to capture, store, transfer, and exchange knowledge through using several types
of clinical vocabularies, or ontologies.
f. Become aware of and navigate vocabulary utilization issues.
Diagnostic Decision Support Systems
a. Understand the medical decision making process.
b. Understand cognitive decision making.
c. Describe the uncertainties associated with clinical decision making.
d. List common psychological biases that negatively impact decision making.
e. Understand challenges associated with CDSS relative to myriads of decisions faced by
medical professionals on the daily basis.
f. Research one academic or commercial implementation of CDSS in detail.
Clinical Trials of Information Intervention
a. Understand the purpose of clinical trials.
b. Become familiar with basic types of clinical trials.
c. Understand why clinical trials are important and what challenges they face.
d. Understand the relationship between clinical trials and CDSS; know why trials are reviewed
as part of the CDSS course.
e. Learn how to use software to build expert systems that represent foundation for knowledge
acquisition and more sophisticated CDSS.
The Future of CDSS and Related Technologies: Genomics, Genomic Algorithms, and Genome
Sequencing
a. Understand the basic concepts of genetics, genome-wide association studies, and genome
sequencing.
b. Analyze strengths and weaknesses of pharmacogenomics in this field’s current state, along
with far-reaching consequences of breakthroughs within this domain of discovery
c. Discover the promises of genome sequencing; analyze past failures, current challenges, and
future potential.
d. Review highlights of progress the cutting-edge imaging technologies, including brain mapping
and low-radiation scans.
e. Build a basic expert knowledge system advising users how to perform simple operational
tasks.
Ethical and Legal Issues in Decision Support
a. Discuss ethical concerns associated with CDSS.
b. Outline legal issues with design and implementation of CDSS, i.e. liability and negligence
c. Discuss exposure of CDSS application vendors to malpractice lawsuits.
d. Review FDA regulation of CDSS and as a service or product
e. Discuss copyright and patent in relation to CDSS
f. Outline the ‘medical device’ status of CDSS in healthcare markets
g. Understand the software market landscape since popularization of various easily accessible
and inexpensive “apps” that often pretend to serve as authoritative software solutions for
complex clinical problems
h. Recognize opportunities for data convergence and risks associated with explosion of “big
data” in healthcare.
i.
3.
4.
5.
6.
7.
8/10
i.
Work on a comprehensive CDSS programming exercise that involves defining a clinical
workflow and programming it in the Exsys Corvid application.
8. Conceptual Foundations for Commercial CDSS Implementations
a. Define quality goals of CDSS in the healthcare industry setting, including health outcomes
and quality metrics.
b. Define business definitions of care success and understand hospital/practice expectations
from commercial CDSS implementations.
c. List common pitfall and political challenges in CDSS implementations
d. Learn how to determine software requirements and identify vendors that can satisfy hospital’s
information technology needs.
e. Complete the exercise of building a simple CDSS solving one clinical workflow problem.
9. Organizing a Successful CDSS Program
a. Learn how to establish foundations for successful commercial CDSS implementation,
including building a team, getting the right individuals from various levels in the organization
involved, set strategic and detailed targets for CDSS, analyze clinical expectations in terms of
the outcomes, and perform full stakeholder analysis
b. Learn how to start a successful change management program and why it is important.
c. Define shared governance and understand why it is critical to CDSS project success.
d. Become familiar with several project communication strategies.
e. Build a worksheet for establishing a successful CDSS program.
f. Become familiar with one of the medical ontologies and complete a short paper outlining
details, purposes, and strengths.
10. Key CDSS Building Blocks
a. Define CDSS building blocks.
b. Understand the role of CDSS in clinical interventions.
c. Expand the knowledge of clinical data vocabularies most frequently utilized in commercial
settings.
d. Deepen understanding of HL7, its purpose, and the latest CDA/CCD format that enables
comprehensive clinical data exchange and systems interoperability.
e. Learn basic systems integration principles.
f. Understand why measurements are important, from defining initial baseline to quantitative
evaluation of project success.
g. Review and critically assess potential of modern CDSS based on a different approach to
practicing medicine: breakaway from traditional diagnosis methods, digital patient data
storage and exchange, reliance on data convergence to provide physicians with mountains of
summarized knowledge and diagnostic data, and the role of knowledge/content management
in supporting progress of the modern life science fields.
h. Discuss medical ontologies with classmates, discover more ontologies as part of the online
peer-to-peer interaction process, and compare/contrast vocabularies presented in class.
11. Foundations for Effective CDSS Interventions
a. Learn how to identify opportunities for CDSS assistance with solving clinical problems.
b. Learn how to help clinicians prevent medical errors using CDSS.
c. Investigate clinical and patient care quality targets; learn how to incorporate into CDSS.
d. Continue to expand practical skills necessary to be able to set project goals and establish a
baseline.
e. Understand the definition and purpose of order sets, their importance to CPOE and CDSS
success.
f. Define and utilize alerts as an effective intervention method.
g. Learn how to select the right interventions and prioritize project goals among myriads of
critical hospital and quality management needs.
12. CDSS Industry Implementation: Case Analysis
9/10
a. Review, analyze, contrast, and compare two commercial CDSS implementation cases.
13. Practical Implementation of Clinical Interventions with CDSS
a. Learn key steps to succeeding with selecting and implementing CDSS interventions.
b. Discover usability elements that need to be analyzed prior to implementation.
c. Learn how to configure interventions.
d. Understand who needs to be involved with implementation projects.
e. Learn how to plan, implement, measure, and evaluate interventions.
f. Describe common pitfalls in implementing interventions.
g. Begin working on a comprehensive capstone project building a CDSS.
14. The Future of CDSS: Rebooting the Life Sciences Industry
a. Review promises of the new life science technologies.
b. Understand the clash between immediate profit driving big business and future direction of life
sciences and healthcare.
c. Analyze challenges associated with commercial implementations of new technologies
d. Discuss ethical concerns in the process of aggressive rollout of discoveries that rapidly make
their way from laboratory to market.
e. Put all technologies and processes covered in the course in perspective and critically assess
how these taken as a whole will affect the future of disease diagnosis and treatment
f. Understand how knowledge management via CDSS and data convergence will support
fundamental shifts in medicine.
g. Present team discoveries in the field of CDSS, along with theoretical CDSS models
developed as part of the capstone project.
10/10