Syllabus
Master of Science in Health Informatics
HIN 770 : Foundations of Healthcare Data Analytics – Summer 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
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. The discussion board and assignment links for each week will open at the start of the week for submissions.
Discussion Board Posts: 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.
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
| Assignment | Points |
|---|---|
| Discussions (8*3 points each) | 24 |
| Swirl Homework (6*2 points each) | 12 |
| Week 1 Quiz | 5 |
| Week 1 Assignment | 8 |
| Week 2 Assignment | 10 |
| Week 3 Assignment | 7 |
| Week 4 Assignment | 10 |
| Week 5 Assignment | 7 |
| Week 6 Assignment | 10 |
| Week 7 Assignment | 7 |
| Total | 100 |
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 April 28 – May 5 |
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
May 5 – May 12 |
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
May 12 – May 19 |
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
May 19 – May 26 |
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
May 26 – June 2 |
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
June 2 – June 9 |
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
June 9 – June 16 |
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)
June 16 – June 20 |
Data Visualization (Conclusion) |
Week 8 Discussion: Initial post due Friday. Responses due by Sunday. |
Student Resources
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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.
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