Syllabus

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 Jan 8 – Jan 15	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 Jan 15 – Jan 22	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 Jan 22 – Jan 29	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 Jan 29 – Feb 5	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 Feb 5 – Feb 12	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 Feb 12 – Feb 19	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 Feb 19 – Feb 26	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) Feb 26 – Mar 1	Data Visualization (Conclusion)	Week 8 Discussion: Initial post due Friday. Responses due by Sunday.

Weekly Course Schedule

Week 1 – Introduction to Data Analytics and R: Tools, Techniques and Data

Learning Outcomes

• Identify the principles of the data analytics process
• Select a graphical presentations (data visualization diagrams) to answer a specific research question

Course Materials

Read:

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

• Chapters 3 & 5

Kuo, Y., Goodwin, J. S., Chen, N., Lwin, K. K., Baillargeon, J., & Raji, M. A. (2015). Diabetes mellitus care provided by nurse practitioners vs primary care physicians. Journal of the American Geriatrics Society, 63(10), 1980-1988. doi:10.1111/jgs.13662

Swirl Guide

Week 1 Course Survey

Please also review the following resources:

• CRAN (R website): https://cran.r-project.org
• RStudio: https://www.rstudio.com
• Code School: https://www.codeschool.com/courses/try-r
• Data Camp:  https://www.datacamp.com

Assignments

Discussion

Read the following article: 

Kuo, Y., Goodwin, J. S., Chen, N., Lwin, K. K., Baillargeon, J., & Raji, M. A. (2015). Diabetes mellitus care provided by nurse practitioners vs primary care physicians. Journal of the American Geriatrics Society, 63(10), 1980-1988. doi:10.1111/jgs.13662

Do you think that a table is an adequate representation of the data? If not, suggest other visualizations. Support your preference with examples.

Week 1 Quiz

Swirl Homework (Optional in Week 1)

• See Swirl Guide

Week 1 Assignment

Take the following steps to complete your assignment:

• Download RStudio Desktop from the RStudio website. Take a screenshot of the application open on your computer desktop.

Begin to think like a researcher

• Think of a research question on a topic that you would like to study. For example, “what is the effect of smoking on birthweight and gestational age.”
• Identify the setting where you will collect the data and the variables that will be needed to answer the research questions (at least one independent variable and one dependent variable)
• Use Microsoft Excel to build a prototype of the data visualization (i.e., a scatterplot) you would use in the publication of your study. Hint, use dummy data to build the visualization.
• Create a document using Microsoft Word in which you state your research question and explain your rationale for selecting the particular visualization and how it helps you understand the data.

Week 2 – Common R Functions and Language

Learning Outcomes

• Describe the role of data analytics in healthcare and how statistical programming languages like R can solve data analytics challenges
• Use R scripts to display data analytics in R Studio on Patient length of stay (LOS) and discuss the produced results

Course Materials

Read:

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

• Chapter 5

R Language Definition:  https://cran.r-project.org/doc/manuals/r-release/R-lang.pdf

• Read the introduction and familiarize yourself with the table of contents. You may find yourself consulting this document over the remainder of the course, so consider bookmarking it in your browser.

An Introduction to R:  https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf 

• Another useful resource to review and consider bookmarking for future use.

Assignments

Discussion

Identify an area in healthcare that could benefit from health informatics. What are the challenges of obtaining data? Can you always trust the analysis?

Swirl Homework

• See Swirl Guide

Week 2 Assignment

1. Import the Patient LOS file (CSV) into RStudio.
2. Run the lines in the R script (Chapter5 Final.R ) necessary to calculate the mean LOS (LOS means Length of Stay) for all patients,  and create a boxplot.
3. The R script has additional calculations; choose another calculation and display the results.
4. Submit a screenshot of the Rscript you wrote to calculate the mean LOS and to construct the boxplot.
5. Submit a screenshot of the additional calculation you ran.
6. Using Microsoft Word, write a few paragraphs describing your experience running R scripts, state which additional outcome you selected and the corresponding R script you selected. Interpret the results of the R output from the two scripts

Week 3 – CRAN and R Packages

Learning Outcomes

• Identify common analytical packages for statistical analysis in healthcare.
• Navigate CRAN to identify R packages to answer specific health-related questions
• Install R packages into R Studio

Course Materials

Read:

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

• Chapter 5

CRAN and R Packages:  https://cran.r-project.org/  Hint: Look for the packages link on the left side of the page.

Gutiérrez-Sacristán, A., Bravo, À., Giannoula, A., Mayer, M. A., Sanz, F., & Furlong, L. I. (2018). comoRbidity: An R package for the systematic analysis of disease comorbidities. Bioinformatics (Oxford, England), 34(18), 3228-3230. doi:10.1093/bioinformatics/bty315

• Supplementary File

Assignments

Week 3 Discussion

For this week’s discussion read the following article and answer the questions below:

“comoRbidity an R package for the systematic analysis of disease comorbidities” and the supplementary file information.

How could this type of R package improve healthcare quality and enhance decision making? As you respond to this question, also consider: Why was comoRbidity created?  What types of data analysis questions can the R package comoRbidity solve?

Swirl Homework

• See Swirl Guide

Week 3 Assignment

Scenario: You are employed at a local hospital as a health informatics analyst.  The hospital has experienced a 200% increase in emergency room (ER) visits over the past 12 months.  This has caused ER wait times to increase from 25 minutes to 90 minutes.

Initial analysis of of the data showed that many of the ER visits were due to routine medical care or minor illness/injury.  The hospital wants to investigate the possibility of opening urgent care centers in the city to reduce the number of non-emergent ER visits.  The current question at hand is: where should these clinics be located, and do we have certain locations within the city where large populations are being seen in the ER for non-emergent conditions?

Instructions: Knowing the type of data that is collected when you present at the ER, select an R package from the list on cran.r-project.org that could assist you in answering the aforementioned questions and can be used to show your analysis to hospital administration. 

Install the R package that you selected and capture a screenshot of R Studio once the package has been installed.  Discuss how you would use the selected R package to answer the questions above and what types of data would you need (submit your work as a Microsoft Word document).

• Part 1: Turn in the list of variables you will want to analyze for this project (in a Word document). This will allow your instructor to give you feedback. (1 point)
• Part 2: Submit a screenshot of the R package you installed, and a Word document explaining the types of data you would use for analysis. (8 points)

Week 4 – Data Preparation (cleansing and data normalization)

Learning Outcomes

• Discuss the importance of data cleansing and data normalization
• Identify the challenges with data cleansing and data normalization
• Prepare a data-set to ensure proper data analysis and visualization

Course Materials

Read:

CMS Public Data website:  https://data.cms.gov/  This website is one source for publicly available health data.

Data.CMS.gov: Inpatient Prospective Payment System (IPPS) Provider Summary for the Top 100 Diagnosis-Related Groups (DRG) – FY2013 – https://data.cms.gov/Medicare-Inpatient/Inpatient-Prospective-Payment-System-IPPS-Provider/kd35-nmmt

Medical big data: promise and challenges: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5331970/

Data Cleaning: Detecting, Diagnosing, and editing data abnormalities: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1198040/ 

Assignments

Week 4 Discussion

For this week’s discussion, read the two scholarly articles that address data cleansing and data normalization, and then discuss the following questions.  

1. What are the challenges with data cleaning and data harmonization?  
2. Without proper data cleaning and normalization, what types of problems might you incur with data analysis and visualization? 
3. Finally, do you think it would be better to clean the data before migrating it to R or to find an R package to try and clean the data once it’s inside R Studio?

Swirl Homework

• See Swirl Guide

Week 4 Assignment

Scenario: For this assignment you are employed by CMS (Centers for Medicare and Medicaid Services) as a senior data analyst and you are being asked to compare and contrast chronic disease data from Maine and Alabama.

Instructions: To start, download (as a CSV file) the FY 2013 Inpatient Prospective Payment System (IPPS) Provider Summary for the Top 100 Diagnosis-Related Groups (https://data.cms.gov/Medicare-Inpatient/Inpatient-Prospective-Payment-System-IPPS-Provider/kd35-nmmt).   

Before you can analyze the data, you need to prepare (clean) the data to meet the specifications of the chronic disease data report.  These specifications include:

• The analysis should only include data from the following DRGS (Centers for Medicare and Medicaid Services) (only Maine and Alabama): 191, 192, 280, 281, 282, 291, 292, 293, 300, 303, 305, 392, 637, 638, 682, 683, and 684.
• Include DRGS with a total discharge day total of  ≥ 15 days and an average Medicare payment of ≥ $3,500.00.
• Sort the data by state and then by city.

There are many ways to achieve the final dataset. Submit your dataset as a Microsoft Excel spreadsheet. There should be 513 rows in the file you submit. Include a brief paragraph (Word document) of the process you used to select the data. Note, some DRGs may not be present in both states.

Week 5 – R Scripts (functions) and Data Analysis – Part 1

Learning Outcomes

• Differentiate the economic impact of DRG data on healthcare delivery for Maine and Alabama
• Summarize the analytical techniques used to evaluate coded healthcare data
• Present aggregate data through exploratory data analysis

Course Materials

Read:

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

• Chapter 5

Assignments

Week 5 Discussion

Perform a web search for “Anscombe’s Quartet,” a collection of four very small sets of data with identical (or near-identical) numerical statistics, but very different visual characteristics when graphed as scatterplots. (See Anscombe (1973) for the original paper.) Why it is important to visualize data before any statistical analyses are performed?

Swirl Homework

• See Swirl Guide

Week 5 Assignment

For this week’s assignment, import your CSV file (from week 4 — Alabama and Maine) into R Studio.  

1. Begin following the steps (step 2) in your textbook (page 89). Remember that R is case sensitive, if you get any errors, check your spelling, correct, and retry!
2. Follow the same naming convention except for ipps_2011id or ipps_2012 since zip code will become the unique identifier in your data set.
3. Follow the steps in your book, stopping halfway through page 92 (stop at the point where it asks you to install ggplot2).  
4. Run each of the commands and create a screenshot of R Studio after you generate data from each command.  
5. Run the scripts in your textbook (beginning on page 89 midway through page 92). Based on the results of your calculations, do payments from non-Medicare sources differ in interesting and meaningful ways by DRG, state (Maine or Alabama), or both? How so?

Week 6 – R Scripts (functions) and Data Analysis – Part 2

Learning Outcomes

• Use R commands to generate descriptive statistics
• Apply descriptive statistical outputs to make healthcare informed decisions

Course Materials

Read:

Introduction to dplyr:  https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.html

Watch 

SuperDataScience. (Aug 17, 2017). R PROGRAMMING dplyr BASICS – summarize, group_by, select, mutate, filter, arrange. Retrieved from https://www.youtube.com/watch?v=BaFkbNOaof8.

Assignments

Week 6 Discussion

With your dataset in mind, what can descriptive statistics tell us about the data?  What kind of inferences can we draw from the data?  What are the limits to descriptive data within your dataset? 

Swirl Homework

• See Swirl Guide

Week 6 Assignment

Use R studio and the R package, dplyr, answer the following questions based on the modified data set you created last week. Show your work through screenshots.

• What is the mean discharge days for DRG 637 in Maine and also Alabama?
  • Hint: you will want to create an additional column containing just the DRG number. In Excel you would use the LEFT function. Look at the R substring function.
• What is the mean Medicare payments for DRG 280 in Alabama?  
• Produce a five number summary on the DRG 280 for Medicare payments in Maine. You will use the fivenum function and will produce an output for minimum, 1st quartile, median, 3rd quartile and maximum.
  • Hint: fivenum function can only be used with a variable, not a dataset column. How will you copy data from a column into a variable? Note: columns within a dataset are referenced as dataset$column

Week 7 – R Scripts (functions) and Data Analysis – Part 3

Learning Outcomes

• Describe the process of combining multiple data types for carrying out analytical procedures
• Cleanse and normalize the data in Excel prior to data analysis in R studio
• Generate chi-squared test to assess the relationship or healthcare practice characteristics to EHR adoption

Course Materials

Read:

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

• Chapter 5

Download:

You will use the file below in your assignment for this week. 

Week 7 – Chapter 7.csv

Assignments

Week 7 Discussion

​Why is it important to identify erroneous values in the data set, harmonize measures, and recode data fields prior to analysis? As part of your response, provide examples of potential problems with the analysis if these issues were not corrected (think of any type of healthcare data set: admission data, cancer registry, etc.).

Swirl Homework

• See Swirl Guide

Week 7 Assignment

Note: The instructions in your book may not perfectly match the version of Excel you are using. Some icons or language may differs slightly, but the functionality is the same.

For this week’s assignment you will use the EHR incentive Excel file.

• Begin on page 106 of your textbook with step 2 (data preparation).  
• First, clean the data in Excel following the book guidelines.  You will submit your cleaned Excel data.
• Next, import the cleaned Excel data (step 3) into R Studio and complete all the steps and commands ending on page 117 of your textbook.  
• Take screenshots after you generate each output and submit them for this assignment.

Week 8 – Data Visualization (Conclusion)

Learning Outcomes

• Explain how data visualization techniques can improve decision making.
• Discuss the use of exploratory data analysis and data visualization techniques to enhance interpretation of health data. 

Course Materials

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

• Chapter 6

Assignment

Week 8 Discussion

Imagine you have recently been hired as a senior healthcare data analyst at the Maine Department of Public Health. You have noticed that most of their reports and data analysis are generated and published using traditional text generation. You are scheduled to meet with the chief operating officer (COO) to discuss how improvements to these reports could benefit decision making.  Most of the reports are longitudinal in nature, where health data is generated and compared over months or years.  

How would you convince the COO that data visualization could enhance data interpretation and lead to more informed decision making?  Discuss the benefits of data visualization and provide examples of which types of data visualization would work best given the data that is tracked by the department.

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