Clinical Informatics Elective
Title of Elective: Clinical Informatics Elective
Elective Year: 4th year elective
Type of Elective: Non-clinical
Elective site: UT Health Science Campus
Course Number: MDED 714
Blocks available: variable, based on approval of faculty
Number of students per block: 2 students, based on approval of faculty.
Faculty:
Dr. R. Ryan Sadeghian, MD, MBA, MSc, FAAPL, FACHDM, FHIMSS, FAAP
Chief Medical Information Officer (CMIO)
Email: reza.sadeghian@utoledo.edu
Phone: 419.383.5205 (office), 412.639.7161 (cell)
Length of Elective: 4 weeks
Course description:
The primary goal of this elective is to provide a comprehensive introduction to Clinical Informatics, emphasizing its critical role in modern healthcare delivery, the integration of emerging technologies, and the application of data-driven decision-making in improving patient care.
Objectives (Based off DECODE Domains)
1. Professionalism, Ethics, and Accessibility in Digital Health
- Apply principles of digital professionalism, patient privacy, cybersecurity, and accessibility. (PB-1, MK-5)
- Analyze and evaluate ethical and social implications of digital health adoption. (PB-1, MK-3, PBL-2)
2. Patient & Population Digital Health
- Describe and critically appraise digital health tools (telehealth, wearables, mobile apps, remote monitoring). (MK-5, PBL-2, SBP-4)
- Identify challenges in digital health literacy and propose solutions that promote access and inclusion.. (SBP-2, SBP-6)
3. Health Information Systems
- Explain the structure and function of EHRs, health information exchanges, and interoperability standards (MK-5, PC-4)
- Analyze usability and workflow integration challenges. (SBP-4, PBL-2)
4. Health Data Science and Analytics
- Interpret dashboards and data visualizations for clinical and population health. (PC-6, PBL-2)
- Assess opportunities and limitations of predictive analytics, AI, and ML in healthcare. (MK-7, PBL-2, SBP-3)
5. Emerging Technologies & Innovation
- Identify and evaluate emerging digital health technologies (AI, precision medicine, genomics). (MK-4, PBL-2)
- Propose innovative applications of informatics to improve patient care. (PBL-2, SBP-4, PC-4)
Elective Structure
Instructional Methods
Didactics and Seminars (20%)
- Attend lectures on core clinical informatics topics.
- Engage in interactive workshops on data analytics, AI tools, and their applications in clinical settings.
Hands-on Experience (30%)
- Shadow IT staff and clinical informaticists.
- Participate in EHR optimization sessions.
- Engage in data analysis projects using real (de-identified) healthcare data.
Project Work (30%)
- Design and implement a small-scale informatics project.
- Present project findings to the clinical informatics team.
Journal Club and Literature Review (10%)
- Review and discuss recent publications in clinical informatics.
- Explore case studies of successful health IT implementations.
Interdisciplinary Collaboration (10%)
- Attend meetings with clinical teams to understand their IT needs.
- Participate in IT governance committee meetings.
Assessment Methods
- Participation in didactics and hands-on activities (40%)
- Active participation in all lectures, workshops, and shadowing experiences is required.
- Reflection Assignment (20%)
- A 1 page reflection on equity, ethics, or workflow in digital health
- Final Project presentation and report (40%)
- Trainees will design a clinical informatics project and present their findings at the end of the elective.
Optional Reading
- "Biomedical Informatics: Computer Applications in Health Care and Biomedicine" by Edward H. Shortliffe and James J. Cimino
- Executive Evolution: A Guide for the Modern Leaders by Dr. R. Ryan Sadeghian
- Rebooting Care: Prescribing AI to Prevent Physician Burnout by Dr. R. Ryan Sadeghian
- The Talent Exodus: How Poor Leadership Decisions Erode Top Teams by Dr. R. Ryan Sadeghian
- Intelligent Healing: Clinicians at the AI Forefront by Dr. R. Ryan Sadeghian
(These books are optional) - Additional readings provided by the instructor
- Selected articles from JAMIA, npj Digital Medicine, and other relevant journals
Optional Learning Opportunities
- Attend health IT conferences or webinars
- Participate in online courses on data science or machine learning in healthcare
Prerequisites: Successful completion of the pre-clerkship curriculum
Administrative Contact:
Maura Luettke
Email: maura.luettke@utoledo.edu
ECC Approved
November 2025