INFM 214-10
Health Data Analytics
Summer 2020 Syllabus

Harry J. Martin

Office: office location
Phone: (408) 644-2497
Office Hours:
Virtual office hours. Telephone and in-person advising by appointment

Syllabus Links
Program Learning Outcomes (PLOs)
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Canvas Information: Courses will be available beginning June 01, 2020, 6 am PT unless you are taking an intensive or a one-unit or two-unit class that starts on a different day. In that case, the class will open on the first day that the class meets.

You will be enrolled in the Canvas site automatically.

Course Description

Exploration of healthcare informatics and its relation to health information technology. Students will apply basic knowledge and skills from healthcare data mining, data science, data management, and professional project management to address practical healthcare business and clinical intelligence issues.


Assignment Portion of Course Grade Due Date CLOs

Class Participation


15% Due by the end of each week (Sunday at 11:59 p.m.) 1,2,3,9,10

Writing Assignments

30% Due Dates are listed in the Course Schedule  CLOs 1,2,4,5,8



10% Due by the end of each week (Sunday at 11:59 p.m.)  1,3,6,7,10

Course Project


25% August 7  1,2,4,5,8

Two Exams (one of which is the final exam)


20% Exam Dates are listed in the Course Schedule  1,3,6,7

Course Schedule


Topics, Readings, Assignments, Deadlines

6/1 - 6/7

Introductions & Course Overview

Introduction to Healthcare Data Analytics: CLOs 1

Quiz 01   Due: June 7


  • Reddy, C. K., & Aggarwal, C. C. (2015). Chapter 1: An Introduction to Healthcare Analytics. In C. K. Reddy & C. C. Aggarwal (Eds.), Healthcare data analytics (pp. 1- 18). Boca Raton: CRC Press, Taylor & Francis Group.


6/8 - 6/14

Electronic Health Records

History of Health Information Technology in the U.S.  CLOs: 1,2

Networking, Interoperable HIT, Health Information Exchange: CLOs 1,2,4,5,8

Quiz 02  Due: June 14


  • Rahnan, R., & Reddy, C. K. (2015). Chapter 2: Electronic Health Records: A Survey. In C. K. Reddy & C. C. Aggarwal (Eds.), Healthcare data analytics (pp. 21-60). Boca Raton: CRC Press, Taylor & Francis Group.
  • TBA - Networking, Interoperable HIT, Health Information Exchange


6/15 - 6/21

Public Health IT - Data Analytics for Pervasive Health: CLOs 1,6,9

Population Health: CLOs 9

Quiz 03  Due: June 21

Writing Assignment 01 DueJune 21

Course Project Topic Selection Due: June 21


  • Acampora, G., Cook, D. J., Rashidi, P., & Vasilakos, A. V. (2015). Chapter 16: Data Analytics for Pervasive Health. In C. K. Reddy & C. C. Aggarwal (Eds.), Healthcare data analytics (pp. 21-60). Boca Raton: CRC Press, Taylor & Francis Group.
  • TBA - Population Health


6/22 - 6/28

Mining of Sensor Data in Healthcare - A Survey

Information Retrieval for Healthcare: CLOs 10

Quiz 04  Due: June 28

Course Project Topic Approval Due: June 28


  • Sow, D., Turaga, K. K., Turaga, D. S., & Schmidt, M. (2015). Chapter 4: Mining of Sensor Data in Healthcare: A Survey. In C. K. Reddy & C. C. Aggarwal (Eds.), Healthcare data analytics (pp. 91-126). Boca Raton: CRC Press, Taylor & Francis Group.
  • Hersh, W. R. (2015). Chapter 14:  Information Retrieval for Healthcare. In C. K. Reddy & C. C. Aggarwal (Eds.), Healthcare data analytics (pp. 467-506). Boca Raton: CRC Press, Taylor & Francis Group.


6/29 - 7/5

Clinical Decision Support Systems CLOs 1,6

Natural Language Processing and Data Mining for Clinical Text: CLOs 3

Quiz 05 Due: July 05

Exam 01 Due: July 05


  • Alther, M., & Reddy, C. K. (2015). Chapter 19: Clinical Decision Support Systems. In C. K. Reddy & C. C. Aggarwal (Eds.), Healthcare data analytics (pp. 625-656). Boca Raton: CRC Press, Taylor & Francis Group
  • Raja, K., & Jonnalagadda, S. R. (2015). Chapter 7: Natural Language Processing and Data Mining for Clinical Text. In C. K. Reddy & C. C. Aggarwal (Eds.), Healthcare data analytics (pp. 219-250). Boca Raton: CRC Press, Taylor & Francis Group.


7/6 - 7/12

Social Media Analytics: CLOs 1,2,3,4,5,6,7,8,10

Visual Analytics for Healthcare: CLOs: 1,2

Quiz 06  Due: July 12

Writing Assignment 02 Due: July 12


  • Kotov, A. (2015). Chapter 9:  Social Media Analytics for Healthcare. In C. K. Reddy & C. C. Aggarwal (Eds.), Healthcare data analytics (pp. 309-339). Boca Raton: CRC Press, Taylor & Francis Group.
  • Gotz, D., Caban, J., & Chen, A. T. (2015). Chapter 12:  Visual Analytics for Healthcare. In C. K. Reddy & C. C. Aggarwal (Eds.), Healthcare data analytics (pp. 403-432). Boca Raton: CRC Press, Taylor & Francis Group.


7/13 - 7/19

Project Management: CLOs 7,10

Improving Healthcare Workflow Processes and Healthcare Quality. CLOs 1,2,4,5,8

Quiz 07 Due: July 19


  • Mills, M. (2016). Project Management Principles for Health Informatiics. In R. Nelson & N. Staggers (Eds.), Health Informatics - E-Book: An Interprofessional Approach (pp. 284-297): Elsevier Health Sciences.
  • TBA - Reading Improving Healthcare Workflow Processes and Healthcare Quality


7/20 - 7/26

Information Security: CLOs 8

Data Standards:  CLOs 10

Quiz 08 Due: July 26


  • Czirr, K., MS, RHIA, CHP, & West, E., RHIA. (2017). In L. B. Harman & F. H. Cornelius (Eds.), Ethical Health Informatics: Challenges and Opportunities: Jones & Bartlett Learning.
  • Imler, T. D., Vreeman, D. J., & Kannry, J. (2016). Healthcare Data Standards and Exchange. In J. T. Finnell & B. E. Dixon (Eds.), Clinical informatics study guide : text and review (pp. 233-253).


7/27 - 8/2

Emerging Technologies

Ethical Considerations

The Path Ahead: AI, Machine Learning, Big Data, Quantum Computing, Ubiquitous Access to Information CLOs 1,3,6,9,10

Quiz 09  Due: Aug 02

Writing Assignment 03  Due: Aug 02

Course Project Presentation Slides  Due: Aug 02


  • TBA


  • Docherty, N., & Fanning, D. (Writers). (2019). In the Age of AI [Video]. In PBS (Producer), Frontline: PBS.


8/3 - 8/7

Course Project Presentations CLOs 1,2,4,5

Quiz 10  Due: Aug 07

Exam 02 (Final) Due: Aug 07

Class Participation

Students are expected to fully participate in the class by reviewing lectures, contributing to discussions, working in small groups, leading student presentations. . Periodically, class participation will be evaluated through short writing assignments and/or exercises. Students must be respectful of each other’s opinions and values.

Writing Assignments

Short writing assignments are one of the most important aspects of the Health Data Analytics course. In addition to reading and writing assignments, we will hear from experts who will share their professional experiences, and we will also consult the literature and news sources to follow what experts are saying about the field and issues pertinent to Analytics. Plan to work on these writing assignments almost every week. The assignments are due on Canvas at 11:59 p.m. of the designated date.  Late  assignments will be accepted with a 20% reduction in grade per 24 hour period that is late.


Periodic quizzes will help you assess your mastery of key concepts and prepare for the final exam. Quizzes will also help you prepare for important class discussions and presentations.  Make-up quizzes are not provided.

Course Project

The primary objective of the class project is for students to use the knowledge they will acquire during the course to identify a Health Data Analytics solution (also known as a “use case”) in a setting with which they are familiar or are planning to pursue as part of their career. The class project involves the preparation and approval of a written project proposal, which is a formalized, written “use case” that describes not only the functional requirements of the proposed solution but also how it adds value to the system (scenario) involved. It also requires an online class presentation of the “use case”. Students will be allotted 10 minutes to present the individual project undertaken to the full class (including Q&A) using Zoom.

Two Exams (one of which is the final exam)

There will be two exams, the last of which is the final. The exams will be based on any content—text, concepts, material, discussions, class presentations and guest speakers during the semester. The final exam will be cumulative. All exams will be administered online via Canvas and will include a combination of multiple choice, short answer, and/or short essay questions.

Assignments Due

Unless otherwise noted, each module begins on Monday and ends on Sunday. Assignments will be due by 11:59 pm (Pacific Time) on the due date.

Other Readings and Multi-media Assignments

Additional readings and multimedia assignments will be announced at least two (2) weeks prior to the start of the module in which they are assigned.  

Course Workload Expectations

Success in this course is based on the expectation that students will spend, for each unit of credit, a minimum of forty-five hours over the length of the course (normally 3 hours per unit per week with 1 of the hours used for lecture) for instruction or preparation/studying or course related activities including but not limited to internships, labs, clinical practica. Other course structures will have equivalent workload expectations as described in the syllabus.

Instructional time may include but is not limited to:
Working on posted modules or lessons prepared by the instructor; discussion forum interactions with the instructor and/or other students; making presentations and getting feedback from the instructor; attending office hours or other synchronous sessions with the instructor.

Student time outside of class:
In any seven-day period, a student is expected to be academically engaged through submitting an academic assignment; taking an exam or an interactive tutorial, or computer-assisted instruction; building websites, blogs, databases, social media presentations; attending a study group;contributing to an academic online discussion; writing papers; reading articles; conducting research; engaging in small group work.

Course Prerequisites

Graduate Standing or Instructor Consent

Course Learning Outcomes

Upon successful completion of the course, students will be able to:

  1. Describe different types of data generated in health care.
  2. Communicate data analysis results.
  3. Discuss the value and approaches of machine learning and natural language processing.
  4. Select a secondary use (re-use) of clinical data and describe its goals and limitations.
  5. Conduct basic data analyses for a specified purpose.
  6. Describe the applications of data analytics in clinical and patient-oriented settings.
  7. Define the learning health system and describe its operations.
  8. Apply the principles of usability to data capture, analysis, and usage.
  9. Describe the myriad stakeholders who need BI and CI information to perform their jobs in the healthcare arena.
  10. Identify methods for receiving, organizing, storing, mining, and formatting data for BI and CI purposes (Business Intelligence and Clinical Intelligence).

SLOs and PLOs

This course supports Informatics SLO 6: Identify and evaluate specific information, data, records, and ethics challenges in a defined specialized context (health, sports, cybersecurity), and apply knowledge and skills from foundation courses to design and implement technical user-centered solutions to the specified informatics problem.

SLO 6 supports the following Informatics Program Learning Outcomes (PLOs):

  • PLO 1 Apply technology informatics skills to solve specific industry data and information management problems, with a focus on usability and designing for users.
  • PLO 2 Evaluate, manage, and develop electronic records programs and applications in a specific organizational setting.
  • PLO 3 Demonstrate strong understanding of security and ethics issues related to informatics, user interface, and inter-professional application of informatics in specific fields by designing and implementing appropriate information assurance and ethics and privacy solutions.
  • PLO 4 Identify user needs, ideate informatics products and services, prototype new concepts, and evaluate a prototype's usability.


Required Textbooks:

  • Reddy, C., & Aggarwal, C. (2015) Healthcare data analytics. CRC Press. Available from Amazon 036757568X.arrow gif indicating link outside sjsu domain

Grading Scale

The standard SJSU School of Information Grading Scale is utilized for all iSchool courses:

97 to 100 A
94 to 96 A minus
91 to 93 B plus
88 to 90 B
85 to 87 B minus
82 to 84 C plus
79 to 81 C
76 to 78 C minus
73 to 75 D plus
70 to 72 D
67 to 69 D minus
Below 67 F


In order to provide consistent guidelines for assessment for graduate level work in the School, these terms are applied to letter grades:

  • C represents Adequate work; a grade of "C" counts for credit for the course;
  • B represents Good work; a grade of "B" clearly meets the standards for graduate level work or undergraduate (for BS-ISDA);
    For core courses in the MLIS program (not MARA, Informatics, BS-ISDA) — INFO 200, INFO 202, INFO 204 — the iSchool requires that students earn a B in the course. If the grade is less than B (B- or lower) after the first attempt you will be placed on administrative probation. You must repeat the class if you wish to stay in the program. If - on the second attempt - you do not pass the class with a grade of B or better (not B- but B) you will be disqualified.
  • A represents Exceptional work; a grade of "A" will be assigned for outstanding work only.

Graduate Students are advised that it is their responsibility to maintain a 3.0 Grade Point Average (GPA). Undergraduates must maintain a 2.0 Grade Point Average (GPA).

University Policies

Per University Policy S16-9, university-wide policy information relevant to all courses, such as academic integrity, accommodations, etc. will be available on Office of Graduate and Undergraduate Programs' Syllabus Information web page at: Make sure to visit this page, review and be familiar with these university policies and resources.

In order to request an accommodation in a class please contact the Accessible Education Center and register via the MyAEC portal.

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