Biomedical Informatics
Course Description
Biomedical Informatics (BMI) is an exciting and rapidly evolving interdisciplinary field that sits at the crossroads of healthcare, biomedicine, artificial intelligence, and data science. As a relatively young discipline, BMI is already making significant and profound impacts on biomedical research and healthcare practices.
This course is designed to offer a comprehensive introduction to the foundational principles and selected focus topics (e.g., bioinformatics and biomedical AI) within BMI. Through a combination of web enhanced lectures and exercises, students will gain a solid understanding of how to leverage computational and data-driven approaches to improve patient care, enhance medical research, and drive innovations in the biomedical sciences.
By the end of the course, students will be well-equipped with the knowledge and skills necessary to contribute to the advancement of biomedical informatics, positioning them at the forefront of this transformative field.
Course Details
| Field | Details |
|---|---|
| Course Number | BIOM 505, Section 004 |
| Credits | 3 Credit Hours |
| Instructor | Jack Smith, MD, PhD |
| jacwsmith@health.unm.edu | |
| Office Phone | 832-377-1328 |
| Office Hours | TBD |
| Meeting Days & Time | Tuesday / Thursday, 4:00 – 6:30 PM MDT |
| Location | Domenici Center for Health Sciences Education, Room 1731 & Zoom (TBD) |
| Term | Summer 2026 |
| CRN | 14367 |
| Instructional Method | Web-enhanced blended format (synchronous Zoom sessions + Canvas) |
Course Goals
The goal of this course is to develop a broad knowledge base and experience with the filed of Biomedical Informatics and the associated healthcare computer systems The student will develop a basic understanding of the field of Biomedical Informatics in terms of the domains and methods of the field and the computational approaches and systems associated with both research and fielded healthcare systems.
The course provides content which will assist learners in more advanced classes in Biomedical Informatics and Data Sciences in Healthcare.
Student Learning Outcomes
| Upon successfully completing this course, students will be able to: | |
|---|---|
| 1 | Understand the foundations, history, and key topics of biomedical informatics |
| 2 | Apply theories and technologies of biomedical informatics to real-world problems in healthcare |
| 3 | Discuss the advantages and disadvantages of using information science technology in biomedicine and healthcare |
| 4 | Read and engage with contemporary informatics scientific literature |
Textbooks & Materials
Recommended Text
Shortliffe, E. H. & Cimino, J. J. (2021). Biomedical Informatics: Computer Applications in Health Care and Biomedicine (5th Edition). New York, NY: Springer.
Additional Materials
Instructor-provided handouts and links
Technology Requirements
Students should have access to a computer with an Internet connection and a current mainstream web browser compatible with HTML5 (Safari, Chrome, Edge, or Firefox).
Instructional Method
This course adopts a module-based blended format, incorporating both synchronous and asynchronous components. Classes will meet weekly online on a specific day and time for lectures, discussions, and presentations. The course will be web enhanced with the use of Canvas. For Summer 2026 the weekly meetings will occur via Zoom.
- Zoom: TBD
- Meeting ID: TBD
- Password:TBD
The activities for each week should take you about 8 hours, depending on your present study skills and previous experience with graduate education, technology, and on-line learning.
Course Requirements
In this course, there will be a quiz most every week, a midterm exam, and a final exam, all of which are mandatory. All tests are in the form of online, multiple choice exams. They are all !open-book and open-notes” but must be your sole, unaided work. You may seek clarifications from the instructor
Grading Scale
– standard letter grading scale:
| Grade | Range |
|---|---|
| A | 90 – 100 |
| B | 80 – 89 |
| C | 70 – 79 |
| D | 60 – 69 |
| F | Below 60 |
| I | Incomplete |
Grading Assignments
Letter grades will be assigned based on the percentage of total points received (as above). Incomplete is given only when situations outside of the student”s control occur. School policy mandates that incompletes must be completed by the end of the following semester. Incomplete’s that are not completed by the end of the next semester turn into an F automatically.
Due to the nature of this course, your final class grade will largely be based on the results of all the reading assignments and activities (weekly quizzes, midterm exam, final exam) that are designed to reflect your understanding of the course content. Satisfactorily completing all of the assigned readings and activities on time will enable you to achieve the objectives for this course.
Grade Distribution
| Requirement | Percentage |
|---|---|
| Quizzes & Activities | 50% |
| Midterm Exam | 20% |
| Final Exam | 30% |
| Total | 100% |
Late Work Policy
Late work is generally not accepted for full credit. Work submitted as makeup for an excused absence is not considered late and is exempt from this policy.
Course Schedule
The schedule is subject to change. Minor changes will be announced in class; major changes will be provided in writing.
| Wk | Date | Topic | Readings / Activities |
|---|---|---|---|
| 1 | Jun 2 | What is Biomedical Informatics? | Shortliffe Text, Chapters 1 & 30 |
| 1 | Jun 4 | Theory of Computation, Algorithms & Programming | Instructor Handouts / Links |
| 2 | Jun 9 | Theory of Computation, Algorithms & Programming (cont.) | Instructor Handouts / Links |
| 2 | Jun 11 | Clinical Information Systems & EHRs | Shortliffe Text, Chapters 2 & 14; Instructor Handouts / Links |
| 3 | Jun 16 | Probabilistic Reasoning in Medicine | Shortliffe Text, Chapter 3 |
| 3 | Jun 18 | Big Data in Biomedicine | Instructor Handouts / Links |
| 4 | Jun 23 | Securing Data & Protecting Health Information | Instructor Handouts / Links |
| 4 | Jun 25 | Midterm Exam | Online exam — covers Weeks 1–4 |
| 5 | Jun 30 | Data Display & Information Visualization | Shortliffe Text, Chapters 4 & 5; Instructor Handouts / Links |
| 5 | Jul 2 | Clinical Decision Support Systems | Shortliffe Text, Chapter 24 |
| 6 | Jul 7 | Bioinformatics | Shortliffe Text, Chapters 9 & 26 |
| 6 | Jul 9 | Public Health Informatics | Shortliffe Text, Chapter 18 |
| 7 | Jul 14 | Telehealth & Mobile Health | Shortliffe Text, Chapters 20 & 12 |
| 7 | Jul 16 | Information Retrieval & NLP | Shortliffe Text, Chapters 23 & 8 |
| 8 | Jul 21 | AI & Deep Learning in Biomedicine | Instructor Handouts / Links |
| 9 | Jul 23 | AI & Deep Learning in Biomedicine (cont.) | Instructor Handouts / Links |
| 9 | Jul 28 | Final Exam | Online exam — covers all course material |
University Policies & Statements
Accommodation Statement
“In accordance with University Policy 2310 and the Americans with Disabilities Act (ADA), academic accommodations may be made for any student who notifies the instructor of the need for an accommodation. It is imperative that you take the initiative to bring such needs to the instructor’s attention, as he/she are not legally permitted to inquire. Students who may require assistance in emergency evacuations should contact the instructor as to the most appropriate procedures to follow. Contact Accessibility Resource Center at 277-3506 for additional information.”
Title IX Statement
In an effort to meet obligations under Title IX, UNM faculty, Teaching Assistants, and Graduate Assistants are considered “responsible employees” by the Department of Education (see pg 15 – http:// www2.ed.gov/about/offices/list/ocr/docs/qa-201404-title-ix.pdf). This designation requires that any report of gender discrimination which includes sexual harassment, sexual misconduct and sexual violence made to a faculty member, TA, or GA must be reported to the Title IX Coordinator at the Office of Equal Opportunity (oeo.unm.edu). For more information on the campus policy regarding sexual misconduct, see: https://policy.unm.edu/university-policies/2000/2740.html
Academic Integrity Statement
Each student is expected to maintain the highest standards of honesty and integrity in academic and professional matters. The University reserves the right to take disciplinary action, up to and including dismissal, against any student who is found guilty of academic dishonesty or otherwise fails to meet the standards. Any student judged to have engaged in academic dishonesty in course work may receive a reduced or failing grade for the work in question and/or for the course.
Academic dishonesty includes, but is not limited to, dishonesty in quizzes, tests, or assignments; claiming credit for work not done or done by others; hindering the academic work of other students; misrepresenting academic or professional qualifications within or without the University; and nondisclosure or misrepresentation in filling out applications or other University records.