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.
After successfully completing this course, students will be able to
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.
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.
Student projects will include advanced methods to load, analyze, interpret and predict variables included in a dataset. Tasks within projects include:
Your grade in this course will be determined by the following criteria:
Assignment | Points |
---|---|
Discussions (5 points each, once per week) | 40 |
Week 1 Assignment | 6 |
Week 2 Assignment | 10 |
Week 3 Assignment | 7 |
Week 4 Assignment | 10 |
Week 5 Assignment | 10 |
Week 6 Assignment | 7 |
Week 7 Assignment | 10 |
Total | 100 |
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 |
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.
Learning Modules | Topics | Assignments and Due Dates |
Week 1 2/27 – 3/6 |
Managing unstructured data in healthcare settings |
Introductory Discussion Week 1 Assignment – Wednesday 3/6 |
Week 2 3/6 – 3/13 |
Advanced machine learning in healthcare |
Discussion – Initial post by Sunday 3/10, responses by Wednesday 3/13 Week 2 Assignment – Wednesday 3/13 |
Week 3 3/13 – 3/20 |
Exploratory data analysis and visualization for medicare severity diagnosis related group |
Discussion -Initial post by Sunday 3/17, responses by Wednesday 3/20 Week 3 Assignment – Wednesday 3/20 |
Week 4 3/20 – 3/27 |
Exploratory data analysis and visualization for medicare severity diagnosis related group (continued) |
Discussion – Initial post by Sunday 3/24, responses by Wednesday 3/27 Week 4 Assignment – Wednesday 3/27 |
Week 5 3/27 – 4/3 |
Understanding comparative effectiveness research |
Discussion – Initial post by Sunday 3/31, responses by Wednesday 4/3 Week 5 Assignment – Wednesday 4/3 |
Week 6 4/3 – 4/10 |
Using data mining techniques to predict healthcare-associated infections (Part 1) |
Discussion – Initial post by Sunday 4/7, responses by Wednesday 4/10 Week 6 Assignment – Wednesday 4/10 |
Week 7 4/10 – 4/17 |
Using data mining techniques to predict healthcare-associated infections (Part 2) |
Discussion – Initial post by Sunday 4/14, responses by Wednesday 4/17 Week 7 Assignment – Wednesday 4/17 |
Week 8 4/17 – 4/21 |
The future of Artificial Intelligence in healthcare |
Discussion – Initial post by Friday 4/19, responses by Sunday 4/21 |
Please use this forum to introduce yourselves to your instructor and your classmates. Briefly share your academic and professional backgrounds. What are you most interested in learning about during this course? What questions do you hope to have answered?
Unstructured data, that is content found in free text fields within the EHR, contain data valuable to healthcare organizations. Natural language processing (NLP) is a valuable tool to mine this data. For this discussion post, investigate the value and potential opportunities for NLP as described within the scientific literature. How can you balance the advantages of unstructured vs. structured data in the application of healthcare analytics? Include at least 2 primary journal sources in your post.
This exercise will help you to understand how to select, process and identify specific text terms for finding information from large unstructured datasets.
For this week’s assignment:
Deliverables:
After reading the two articles on “Artificial Intelligence powers digital medicine” and “Big data and machine learning in Healthcare,” please discuss the following topics
Write a 2-page (excluding references) paper on the following topic:
Identify and reference two primary journal articles in which an artificial intelligence (AI) method has already been used in some sort of healthcare model or application.
Identify and describe the statistical methods used. Given your knowledge of AI and statistical testing, do the authors’ research methods appear to be sound? Describe in your own words the future opportunities for research and application of AI in healthcare.
Please use APA formatting to cite at least 2 primary journal references (use additional articles, as needed). Use 12-point font, Arial, single spaced.
Deliverables:
One federal initiative to improve healthcare quality is that payors (insurance companies and CMS) may not reimburse hospitals for the cost of care for some hospital acquired conditions (HACs.)
This policy is controversial, as variation in coding practices at hospitals may lead to differences in the inclusion and position of HACs in the list of codes used for Medicare Severity Diagnosis‐Related Group (MS‐DRG) assignment. Please share your ideas about the pros and cons of this practice and discuss how healthcare organizations might comply to operate in a successful manner.
This assignment may seem familiar to you; however, you will be doing the work in R, rather than Excel. Your final goal is to revise the column headers in R Studio, rather than Excel, as you did in HIN-770. You will use the two data files Chapter6_ipps2011.csv and Chapter6_ipps2012.csv files. In this module, you will first import the files into R studio console. You will examine different headers and the overall organization for both files. Then, you will use Table 6.1 from the book to change names of headers of the files to process files for data visualization.
Deliverables:
Conduct a simple internet search for several visualization tools and techniques that use R. List and provide names and links for at least two of the tools that you find — one standalone tool, and another that is a plugin/package to R. List the tools that you investigated. Describe the features of the tools you’ve chosen and the rationale for selecting these two tools over the other tools reviewed. As you read your peers’ posts, respond to at least two others by identifying concerns or advantages of using such a tool in your organization.
In the previous week, you prepared the two datasets. This week, you will use R to explore different data visualizations. Follow the prompts in the book to complete this assignment.
Deliverables:
Screenshots of:
Comparative research is an integral part of hospitals’ operations. Find two examples in the literature that describe how comparative research was used to improve a hospital’s performance. What can institutions who have failing performance do to improve their standing?
For this week’s assignment, you will use logistic regression in R.
Deliverables:
Healthcare data is sparse and not structured. Critics argue that it is not possible to use healthcare data for Big Data purposes or for Artificial Intelligence. What are the challenges of using unstructured healthcare data in an Artificial Intelligence application? Please support your arguments with published evidence. Here are some resources to help you get started:
Install the “rattle” package. The rattle package installation may be different for MAC and Windows operating systems. Load the package and all other supporting installations that need to be executed to have the package install successfully. Troubleshoot any error messages that prevent a successful installation.
Deliverable:
As a future health informatician, what are your concerns, if any, about the use of AI in healthcare? Base your response on your personal opinion, rather than scholarly evidence. This a chance for you to have a hearty discussion about the future of healthcare.
Note: For issues with coding, use the search engine of your choice to look for explanations and solutions to the error.
Deliverables:
The Watson videos describe how Watson works within a binary/transactional industry, such as insurance claims. Patient care decision-making is not that orderly. For example, when prescribing medications, one must consider how the new medications interacts with current medications and whether the medication will work for this patient and this symptom.
Based on the video and your readings from this course, do you believe that Watson can be used to address these types of problems? How much credence should we give Nick Bostrom’s concerns about the ethical implications of AI?
Please note that this week is only 5 days. The class closes on Sunday. No assignments will be accepted after that date.
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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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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.
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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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.
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:
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.