Syllabus

Master of Science in Health Informatics

HIN 775 – Advanced Concepts in Healthcare Data Analytics (Summer B 2023)

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 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/

Recommended:

  • American Psychological Association. (2019). Publication manual of the American Psychological Association (7th ed.). American Psychological Association. ISBN: 978-1433832154. E-text: 978-1433832185

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 modeling 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 the course 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
  • Interpret 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

Week 1: Jun 28 – Jul 5
Week 2: Jul 5 – Jul 12
Week 3: Jul 12 – Jul 19
Week 4: Jul 19 – Jul 26
Week 5: Jul 26 – Aug 2
Week 6: Aug 2 – Aug 9
Week 7: Aug 9 – Aug 16
Week 8: Aug 16 – Aug 20

Learning Modules Topics Assignments and Due Dates
Week 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 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 Understanding comparative effectiveness research

Discussion – Initial post by Sunday, responses by Wednesday

Week 3 Assignment – Wednesday

Week 4 Managing unstructured data in healthcare settings

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

Week 4 Assignment – Wednesday

Week 5 COVID-19: Evaluating cases and mortality rates

Two Discussions – Initial posts by Sunday, responses by Wednesday

 Week 5 Assignment – Wednesday

Week 6 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 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 The future of Artificial Intelligence in healthcare

Discussion – Initial post by Friday, responses optional

 

Student Resources

Online Student Support

Your Student Support Specialist is a resource for you. Please don't hesitate to contact them for assistance, including, but not limited to course planning, current problems or issues in a course, technology concerns, or personal emergencies.

Questions? Visit the Student Support Health Informatics page

APA Style Guide

UNE Libraries:

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The Student Academic Success Center (SASC) offers a range of services to support your academic achievement, including tutoring, writing support, test prep and studying strategies, learning style consultations, and many online resources. To make an appointment for tutoring, writing support, or a learning specialist consultation, go to une.tutortrac.com. To access our online resources, including links, guides, and video tutorials, please visit:

Accommodations

Any student who would like to request, or ask any questions regarding, academic adjustments or accommodations must contact the Student Access Center at (207) 221-4438 or pcstudentaccess@une.edu. Student Access Center staff will evaluate the student's documentation and determine eligibility of accommodation(s) through the Student Access Center registration procedure.

Policies

Technology Requirements

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

Turnitin Originality Check and Plagiarism Detection Tool

The College of Professional Studies uses Turnitin to help deter plagiarism and to foster the proper attribution of sources. Turnitin provides comparative reports for submitted assignments that reflect similarities in other written works. This can include, but is not limited to, previously submitted assignments, internet articles, research journals, and academic databases.

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.

You can learn more about Turnitin in the Turnitin Student quick start guide.

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.

Attendance Policy

Online students are required to submit a graded assignment/discussion prior to Sunday evening at 11:59 pm ET of the first week of the term. If a student does not submit a posting to the graded assignment/discussion prior to Sunday evening at 11:59 pm ET, the student will be automatically dropped from the course for non-participation. Review the full attendance policy.

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.

Student Handbook Online - Policies and Procedures

The policies contained within this document apply to all students in the College of Graduate and Professional Studies. It is each student's responsibility to know the contents of this handbook.

UNE Online Student Handbook

UNE Course Withdrawal

Please contact your student support specialist if you are considering dropping or withdrawing from a course. The last day to drop for 100% tuition refund is the 2nd day of the course. Financial Aid charges may still apply. Students using Financial Aid should contact the Financial Aid Office prior to withdrawing from a course.

Academic Integrity

The University of New England values academic integrity in all aspects of the educational experience. Academic dishonesty in any form undermines this standard and devalues the original contributions of others. It is the responsibility of all members of the University community to actively uphold the integrity of the academy; failure to act, for any reason, is not acceptable. For information about plagiarism and academic misconduct, please visit UNE Plagiarism Policies.

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.