Syllabus

Master of Science in Health Informatics

HIN 775 – Advanced Concepts in Healthcare Data Analytics – Summer B 2020

Credits - 3

Description

Advanced topics in health informatics leverages the concepts introduced in the Foundation course. Students will be exposed to advanced statistics, vast and diverse data sets, and data interpretation and visualization. This course will prepare students for a deeper dive into forecasting, trends, and predictive data modelling. 

Materials

Required

Marc, D. & Sandefer, R. (2016). Data analytics in healthcare research: tools and strategies. Chicago, Illinois: AHIMA Press.

R: https://cran.r-project.org/

R Studio Desktop Open Source License: https://www.rstudio.com/products/rstudio/download/

Learning Objectives and Outcomes

Course Outcomes

After successfully completing this course, students will be able to

  • Explore structured and unstructured healthcare data processing
  • Interpret exploratory data analysis and data visualization
  • Execute dichotomous variables, logistic regression, odds ratio and simple logistic regression (SLR)
  • Apply basic comparative effective research methods to a healthcare organization scenario
  • Apply advanced modelling and statistics to healthcare data to predict health outcomes
  • Explore resources from outside sources to troubleshoot coding related issues
  • Discuss ethical implications of artificial intelligence

Assignments

Please note that all times in the syllabus and in Blackboard refer to Eastern Time. The discussion board and assignment links for each week will open at the start of the week for submissions.

Discussion Boards

Each week there will be a discussion board that addresses a topic within the current module. These assignments will assess your ability to clearly and accurately apply concepts from your readings and from your own experiences. Each week you are expected to submit an initial post and comment on at least 2 other students’ posts. You need to follow APA guidelines for citing any sources you may reference in either your initial post or your response to others. Refer to the Discussion Rubric and weekly discussion question for submission guidelines. Please be sure to follow the individual directions provided with each Discussion Board Prompt, as the requirements may vary from Discussion Board to Discussion Board.

Initial post: You should submit your initial post by 11:59 p.m. Sunday. Your initial post should be approximately 500 words. 

Response to others: You should comment on at least 2 other students’ posts by 11:59 p.m. Wednesday. Your comments to others should be thorough, thoughtful, and they should offer some new content. Do not merely respond with “I agree” or “I disagree.” Engage directly with the ideas of your classmates and briefly mention which part of the post you are responding to.

Weekly Assignments

Student projects will include advanced methods to load, analyze, interpret and predict variables included in a dataset. Tasks within projects include:

  • Loading the dataset
  • Cleansing the dataset
  • Imputing values
  • Creating new variables
  • Running predictive model markup language
  • Interepret advanced statistical results into meaningful conclusions

Grading Policy

Your grade in this course will be determined by the following criteria:

Grade Breakdown

AssignmentPoints
Discussions (5 points each, nine discussions)45
Week 1 Assignment7
Week 2 Assignment9
Week 3 Assignment9
Week 4 Assignment5
Week 5 Assignment9
Week 6 Assignment7
Week 7 Assignment9
Total100

Grade Scale

Grade Points Grade Point Average (GPA)
A 94 – 100% 4.00
A- 90 – 93% 3.75
B+ 87 – 89% 3.50
B 84 – 86% 3.00
B- 80 – 83% 2.75
C+ 77 – 79% 2.50
C 74 – 76% 2.00
C- 70 – 73% 1.75
D 64 – 69% 1.00
F 00 – 63% 0.00

Schedule

Course learning modules are divided into weeks. Each week starts on Wednesday at 12:00 am Eastern Time (ET) and closes on Wednesday at 11:59 pm ET, with the exception of Week 8, which ends on Sunday. All assignments must be submitted by 11:59 pm ET on the due date.

Course Schedule at a Glance

Learning Modules Topics Assignments and Due Dates
Week 1
Jun 24 – Jul 1
Exploratory data analysis and visualization for medicare severity diagnosis related group

Discussion – Initial post by Sunday, responses by Wednesday

 Week 1 Assignment – Wednesday 

Week 2
Jul 1 – Jul 8
Exploratory data analysis and visualization for medicare severity diagnosis related group (continued)

Discussion – Initial post by Sunday, responses by Wednesday

Week 2 Assignment – Wednesday 

Week 3
Jul 8 – Jul 15
Understanding comparative effectiveness research

Discussion – Initial post by Sunday, responses by Wednesday

Week 3 Assignment – Wednesday

Week 4
Jul 15 – Jul 22
Managing unstructured data in healthcare settings

Introductory Discussion
Discussion – Initial post by Sunday, responses by Wednesday

Week 1 Assignment – Wednesday

Week 5
Jul 22 – Jul 29
Advanced machine learning in healthcare

Two Discussions – Initial posts by Sunday, responses by Wednesday

 Week 2 Assignment – Wednesday

Week 6
Jul 29 – Aug 5
Using data mining techniques to predict healthcare-associated infections (Part 1)

Discussion – Initial post by Sunday, responses by Wednesday

Week 6 Assignment – Wednesday

Week 7
Aug 5 – Aug 12
Using data mining techniques to predict healthcare-associated infections (Part 2)

Discussion – Initial post by Sunday, responses by Wednesday

Week 7 Assignment – Wednesday

Week 8
Aug 12 – Aug 16
The future of Artificial Intelligence in healthcare

Discussion – Initial post by Friday, responses by Sunday 

 

Student Resources

Online Student Support

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Questions? Visit the Student Support Health Informatics page

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Policies

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Please review the technical requirements for UNE Online Graduate Programs: Technical Requirements

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Make sure to cite your sources appropriately as well as use your own words in synthesizing information from published literature. Webinars and workshops, included early in your coursework, will help guide best practices in APA citation and academic writing.

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Information Technology Services (ITS)

ITS Contact: Toll Free Help Desk 24 hours/7 days per week at 1-877-518-4673

Course Evaluation Policy

Course surveys are one of the most important tools that University of New England uses for evaluating the quality of your education, and for providing meaningful feedback to instructors on their teaching. In order to assure that the feedback is both comprehensive and precise, we need to receive it from each student for each course. Evaluation access is distributed via UNE email at the beginning of the last week of the course.

Late Policy

Assignments: Late assignments will be accepted up to 3 days late; however, there is a 10% grade reduction (from the total points) for the late submission. After three days the assignment will not be accepted.

Discussion posts: If the initial post is submitted late, but still within the discussion board week, there will be a 10% grade reduction from the total discussion grade (e.g., a 3 point discussion will be reduced by 0.3 points). Any posts submitted after the end of the Discussion Board week will not be graded.

Please make every effort ahead of time to contact your instructor and your student support specialist if you are not able to meet an assignment deadline. Arrangements for extenuating circumstances may be considered by faculty.

Attendance Policy

8 week: Students taking online graduate courses through the College of Professional Studies will be administratively dropped for non-participation if a graded assignment/discussion post is not submitted before Sunday at 11:59 pm ET of the first week of the term. Reinstatement is at the purview of the Dean's Office.

16 week: Students taking online graduate courses through the College of Professional Studies will be administratively dropped for non-participation if a graded assignment/discussion post is not submitted before Friday at 11:59 pm ET of the second week of the term. Reinstatement is at the purview of the Dean's Office.

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UNE Online Student Handbook

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Academic dishonesty includes, but is not limited to the following:

  1. Cheating, copying, or the offering or receiving of unauthorized assistance or information.
  2. Fabrication or falsification of data, results, or sources for papers or reports.
  3. Action which destroys or alters the work of another student.
  4. Multiple submissions of the same paper or report for assignments in more than one course without permission of each instructor.
  5. Plagiarism, the appropriation of records, research, materials, ideas, or the language of other persons or writers and the submission of them as one's own.

Charges of academic dishonesty will be reviewed by the Program Director. Penalties for students found responsible for violations may depend upon the seriousness and circumstances of the violation, the degree of premeditation involved, and/or the student’s previous record of violations. Appeal of a decision may be made to the Dean whose decision will be final. Student appeals will take place through the grievance process outlined in the student handbook.