Oleg Pianykh, PhD,

Assistant Professor of Radiology, Harvard Medical School

Email: opianyATgmail.com (replace AT by @)

Class schedule                           

Thursdays 5:30-7:30 pm (Spring term)

Office hours: by request (can be in person or online)

Course Objective                        

To develop in-depth understanding of Medical Informatics (MI), its goals, standards, applications, and uses in demanding clinical environment.  This course will enable you to identify and solve MI problems in the best possible ways; build, run and optimize complex healthcare processes; do MI research.

Therefore, this course is about: processing clinical data with information science and tools; improving healthcare; doing professional MI projects.

This course is not about: buying hardware, connecting network cables, calling your doctor on iPhone, doing formal paperwork, or typing patient data into Excel spreadsheets – even if you do all this in a hospital.

This course is meant for people with different backgrounds, interested in MI and its applications. You are very welcome to take it. Nonetheless, this is a regular Harvard class and it will require your time and work – please contact me if you have any questions.

Course topics                              

• Introduction: What is MI, and what it is not (don’t think it’s trivial)
• Medical standards (DICOM and HL7) – how they work, and why they make sense
• HIS, RIS, PACS. IHE and workflow integration
• Big Data in hospitals: helpful or confusing?
• Basic medical imaging: acquisition, diagnostic display, enhancement and analysis
• Computed tomography
• Advanced medical imaging: CAD and advanced diagnostic image processing
• Networking and teleradiology
• Fault-tolerance, scalability, and robustness
• Security and confidentiality in medicine
• Clinical modeling and performance optimization
• Bringing MI to hospitals (without making them cry)
• Patient flow analysis. Scheduling problems
• Clinical decision support
• Clinical software development. Medical startups
• Unusual problems: pharaohs, criminals, and pure art

Course Prerequisites                   

• Basic programming skills (experience with any programming language). We will be using Matlab for 70% of our homework – if you do not know Matlab yet, check online tutorials on mathworks.com or YouTube.
• Good understanding of information technology (hardware, software, networking)

Grades, Tests, Homework           

• Your grade is based on homework, class participation, and final exam.

Textbooks                                    

Required

Recommended

These books can be helpful for additional reading:

	H. K. Huang , “PACS and Imaging Informatics: Basic Principles and Applications”, 2010
	Oleg S. Pianykh, “Digital Image Quality in Medicine”, Springer. 2014

Class rules                                  

• Homework – must be submitted on time, or it won’t be graded. All work must be your own, so start early.
• Cell phones, beepers, gadgets, food - outside.
• The Extension School is committed to providing an accessible academic community. The Accessibility Office offers a variety of accommodations and services to students with documented disabilities. Please visit www.extension.harvard.edu/resources-policies/resources/disability-services-accessibility for more information.
• You are responsible for understanding Harvard Extension School policies on academic integrity (www.extension.harvard.edu/resources-policies/student-conduct/academic-integrity) and how to use sources responsibly. Not knowing the rules, misunderstanding the rules, running out of time, submitting the wrong draft, or being overwhelmed with multiple demands are not acceptable excuses. There are no excuses for failure to uphold academic integrity. To support your learning about academic citation rules, please visit the Harvard Extension School Tips to Avoid Plagiarism (www.extension.harvard.edu/resources-policies/resources/tips-avoid-plagiarism), where you'll find links to the Harvard Guide to Using Sources and two free online 15-minute tutorials to test your knowledge of academic citation policy. The tutorials are anonymous open-learning tools.

• Do I need to know programming?

Yes, you should be familiar with code writing. However, you do not have to be a programming guru.

• What kinds of programs we will have to write in this class?

We will be using Matlab to write our code. In fact, Matlab is chosen to make coding easier: what takes a couple of lines in Matlab will take pages in C++ or Java. This will help us concentrate on concepts, not coding.
If you never used Matlab before, I recommend getting familiar with it ahead of time. Just search Google or YouTube for “Matlab introduction” – if you had programming experience before, Matlab should be straightforward.

• Do I need to be math (informatics, statistics, clinical, …) major?

Absolutely not, this is an interdisciplinary class. Nonetheless, math and statistics will be involved, please brush up whatever you've learned about matrices, principal components, Gaussian distribution.

• What will be in the homeworks?

• Who are your typical students?

We have a very diverse audience – with backgrounds varying from computer science to clinical.

• How interactive are your lectures?

• I am interested in … (particular topic). Will it be covered?

Please let me know; your suggestions are welcome.

• About the instructor

My LinkedIn page: www.linkedin.com/pub/oleg-pianykh/10/8b6/823