Syllabus

Master of Science in Health Informatics

HIN 770 – Foundations of Healthcare Data Analytics – Fall A 2021

Credits - 3

Description

R is an open source programming language ideally suited for analysis and visualization. This course will provide students with a foundation in data preparation and preliminary analytics using R which can be applicable for research, quality improvement and industry large-scale data analytics projects. This course will include the following skills: data analysis with publicly available data sets; cleansing and imputing data; descriptive statistics; and data visualization.

Materials

Required

Marc, D. & Sandefer, R. (2016). Data analytics in healthcare research: tools and strategies. American Health Information Management Association 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

  • Analyze publicly available data sets using R
  • Write scripts fluently with R in order to answer basic statistical questions
  • Apply descriptive statistics to data sets
  • Interpret descriptive statistics
  • Select appropriate visualizations given the research question and data set

Assignments

Please note that all times in the syllabus and in course modules refer to Eastern Time.

Discussion Posts: Each week there will be a discussion 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 Prompt, as the requirements may vary.

Homework Assignments (Weeks 1-7): As part of this course, we will be running an interactive program to help you learn the ins-and-outs of R. The program is called swirl (https://swirlstats.com).

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.

Week 1 Assignment: Think of a research question on a topic that you would like to study. Identify the setting where you will collect the data and the variables that will be needed to answer the research questions, and then use Microsoft Excel to build a prototype of the data visualization. Refer to the Week 1 Assignment Rubric and assignment instructions for submission guidelines.

Week 2 Assignment: In this assignment, you will run an R script on a comma delimited file containing patient data. You will calculate the mean length of stay for patients, create a box plot, and run one additional calculation. Refer to the Week 2 Assignment Rubric and assignment instructions for submission guidelines.

Week 3 Assignment: In this assignment, you will select an R package from CRAN to use when answering a question about where urgent care centers should be located. Refer to the Week 3 Assignment Rubric and assignment instructions for submission guidelines.

Week 4 Assignment: This week, you will prepare (clean) a data set to meet certain specifications before analysis. Refer to the Week 4 Assignment Rubric and assignment instructions for submission guidelines.

Week 5 Assignment: Using the data set you prepared in Week 4, you will run commands in R Studio to consider whether payments from non-Medicare sources differ in interesting and meaningful ways by DRG, state, or both. Refer to the Week 5 Assignment Rubric and assignment instructions for submission guidelines.

Week 6 Assignment: This week, you will continue using your modified dataset on Maine and Alabama to determine mean discharge days, mean payments, and a five number summary. Refer to the Week 6 Assignment Rubric and assignment instructions for submission guidelines.

Week 7 Assignment: For your final assignment, you will prepare (clean) an EHR incentive file and run R scripts to obtain specific outputs. Refer to the Week 7 Assignment Rubric and assignment instructions for submission guidelines.

Grading Policy

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

Grade Breakdown

AssignmentPoints
Discussions (8*3 points each)24
Swirl Homework (6*2 points each)12
Week 1 Quiz5
Week 1 Assignment8
Week 2 Assignment10
Week 3 Assignment7
Week 4 Assignment10
Week 5 Assignment7
Week 6 Assignment10
Week 7 Assignment7
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

8/25 – 9/1

Introduction to Data Analytics and R: Tools, Techniques and Data

Week 1 Discussion: Initial post due Sunday. Responses due by Wednesday.

Quiz: Data Analytics Process: Due by Wednesday. You will not be able to take the quiz after this date.

Week 1 Swirl Homework (optional)

Week 1 Assignment: Due by Wednesday.

Week 2

9/1 – 9/8

Common R Functions and Language

Week 2 Discussion: Initial post due Sunday. Responses due by Wednesday.

Week 2 Swirl Homework: Due by Wednesday

Week 2 Assignment: Due by Wednesday.

Week 3

9/8 – 9/15

CRAN and R Packages

Week 3 Discussion: Initial post due Sunday. Responses due by Wednesday.

Week 3 Swirl Homework: Due by Wednesday

Week 3 Assignment: Part 1 due by Sunday; Part 2 due by Wednesday.

Week 4

9/15 – 9/22

Data Preparation (cleansing and data normalization)

Week 4 Discussion: Initial post due Sunday. Responses due by Wednesday.

Week 4 Swirl Homework: Due by Wednesday

Week 4 Assignment: Due by Wednesday.

Week 5

9/22 – 9/29

R Scripts (functions) and Data Analysis – Part 1

Week 5 Discussion: Initial post due Sunday. Responses due by Wednesday.

Week 5 Swirl Homework: Due by Wednesday

Week 5 Assignment: Due by Wednesday.

Week 6

9/29 – 10/6

R Scripts (functions) and Data Analysis – Part 2

Week 6 Discussion: Initial post due Sunday. Responses due by Wednesday.

Week 6 Swirl Homework: Due by Wednesday

Week 6 Assignment: Due by Wednesday.

 

Week 7

10/6 – 10/13

R Scripts (functions) and Data Analysis – Part 3

Week 7 Discussion: Initial post due Sunday. Responses due by Wednesday.

Week 7 Swirl Homework: Due by Wednesday

Week 7 Assignment: Due by Wednesday.

Week 8 (short week)

10/13 – 10/17

Data Visualization (Conclusion)

Week 8 Discussion: Initial post due Friday. Responses due by Sunday.

 

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:

UNE Student Academic Success Center

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.