Health Data Analytics
Summer 2022 Syllabus
Program Learning Outcomes (PLOs)
Canvas Login and Tutorials
Canvas Information: Courses will be available beginning June 01, 2022, at 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.
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|
|15%||Due by the end of each week (Sunday at 11:59 p.m.)||1,2,3,9,10|
|30%||Due Dates are listed in the Course Schedule||1,2,4,5,8|
|10%||Due by the end of each week (Sunday at 11:59 p.m.)||1,3,6,7,10|
Two Exams (one of which is the final exam)
|20%||Exam Dates are listed in the Course Schedule||1,3,6,7|
Topics, Readings, Assignments, Deadlines
Introductions & Course Overview
Introduction to Healthcare Data Analytics: CLOs 1
Quiz 01 Due: June 5
Reddy, C. K., & Aggarwal, C. C. (2015). Chapter 1: An Introduction to
Hartzband, David. (2019). Chapter 1: Introduction -- Data is Essential.
Office of the National Coordinator for Health Information Technology (ONC). (
Electronic Health Records
Quiz 02 Due: June 12
Rahnan, R., & Reddy, C. K. (2015). Chapter 2: Electronic Health Records:
Hartzband, D. (2019). What is data?. In Information Technology and Data
Balgrosky, J. A. (2020). History of health information systems. Understanding
ONC. (2019). Infographic, Connecting the health and care for the nation:
Posnack, S. (2021). A Road Well Traveled – Sunsetting the Interoperability
ONC, HHS. (2016). Executive summary, Connecting the health and care for
Population Health: CLOs 9
Quiz 03 Due: June 19
Writing Assignment 01 Due: June 19
Course Project Topic Selection Due: June 19
Acampora, G., Cook, D. J., Rashidi, P., & Vasilakos, A. V. (2015). Chapter 16:
Hartzband, D. (2019). Data and culture. In Information Technology and Data in
Lewis, N. (2014). Populations, Population Health, and the Evolution of
Kindig, D., & Stoddart, G. (2003). What is population health? Am J Public
Kaminski, M. (2020). What is Population Health? Ten Years On….
Mining of Sensor Data in Healthcare - A Survey
Information Retrieval for Healthcare: CLOs 10
Quiz 04 Due: June 26
Course Project Topic Approval Due: June 26
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.
Hartzband, D. (2019). Data quality. In Information Technology and Data in Healthcare (pp. 31-62). Boca Raton: CRC Press.
Natural Language Processing and Data Mining for Clinical Text: CLOs 3
Quiz 05 Due: July 03
Exam 01 Due: July 03
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.
Hartzband, D. (2019). What is data analysis? In Information Technology and Data in Healthcare (pp. 63-78). Boca Raton: CRC Press.
Quiz 06 Due: July 10
Writing Assignment 02 Due: July 10
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.
Hartzband, D. (2019). The Evolution of infrastructure and applications required for current and near-future HIT. In Information Technology and Data in Healthcare (pp. 79-108). Boca Raton: CRC Press.
Quiz 07 Due: July 17
Mills, M. (2016). Project Management Principles for Health Informatics. In R. Nelson & N. Staggers (Eds.), Health Informatics - E-Book: An Interprofessional Approach (pp. 284-297): Elsevier Health Sciences.
Hartzband, D. (2019). Machine intelligence in healthcare. In Information Technology and Data in Healthcare (pp. 109-134). Boca Raton: CRC Press.
Malhotra, D., & Tableau Software. (2018). How Stamford Hospital Used Data to Save Millions. Achieving Operational Excellence in Healthcare. Retrieved from https://www.tableau.com/learn/webinars/using-data-save-millions-stamford-hospital.
Information Security: CLOs 8
Data Standards: CLOs 10
Quiz 08 Due: July 24
Hartzband, D. (2019). Evolution of data and analysis in healthcare. In Information Technology and Data in Healthcare (pp. 135-156). Boca Raton: CRC Press.
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).
Quiz 09 Due: Jul 31
Writing Assignment 03 Due: Jul 31
Course Project Presentation Slides Due: Jul 31
Hartzband, D. (2019). Summary. In Information Technology and Data in Healthcare (pp. 157-162). Boca Raton: CRC Press.
TBA - Emerging Technologies
TBA - Ethical Considerations
Docherty, N., & Fanning, D. (Writers). (2019). In the Age of AI [Video]. In PBS (Producer), Frontline: PBS.
Quiz 10 Due: Aug 05
Exam 02 (Final) Due: Aug 05
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.
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.
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.
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.
Graduate Standing or Instructor Consent.
Course Learning Outcomes
Upon successful completion of the course, students will be able to:
- Describe different types of data generated in health care.
- Communicate data analysis results.
- Discuss the value and approaches of machine learning and natural language processing.
- Select a secondary use (re-use) of clinical data and describe its goals and limitations.
- Conduct basic data analyses for a specified purpose.
- Describe the applications of data analytics in clinical and patient-oriented settings.
- Define the learning health system and describe its operations.
- Apply the principles of usability to data capture, analysis, and usage.
- Describe the myriad stakeholders who need BI and CI information to perform their jobs in the healthcare arena.
- 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.
- Reddy, C., & Aggarwal, C. (2015) Healthcare data analytics. CRC Press. Available from Amazon 036757568X.
- De Mauro, A. (2021). Data analytics made easy. Packt Publishing. Available through Amazon: 1801074151.
- Hartzband, D. (2020). Information technology and data in healthcare: Using and understanding data. CRC Press. Available through Amazon: 1032082275.
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|
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).
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: https://www.sjsu.edu/curriculum/courses/syllabus-info.php. 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.
Download Adobe Acrobat Reader to access PDF files.
More accessibility resources.