Interrogating Ethics and Justice in Digital Health (BINF G4008)

Course Website
Graduate School of Arts and Sciences, Columbia University, Fall 2022
Course Instructors: Sandra Soo-Jin Lee, PhD and Noémie Elhadad, PhD
Role: Teaching Assistant

Engaging with data is a civic requirement. Technical expertise must include engagement with the ethical issues and policy implications related to emerging data-driven techniques. The biomedical, health, and clinical domains are going through in-depth changes as artificial intelligence and data-driven thinking are becoming inherent to routine processes. How will knowledge production in health data science determine what counts as healthy, normal or disease in individuals and groups? Who will get access to what care and at what price when treatment recommendations are guided by artificial intelligence?

The purpose of this course is to engage students in thinking about the ethical issues and social implications of the creation, analysis and application of data in health. This multidisciplinary course teaches students to situate data technologies within their socio-political contexts and to examine the social life of data and its impact on society. Students become adept at identifying and analyzing how the management and interpretation of data impacts and is impacted by social, economic and political processes.

This collaborative course provides innovative materials and hands-on experience to students through:

  • a series of use cases that reflect ongoing themes in ethics and justice in digital health, and corresponding simulated datasets & computational tools for students to engage with;
  • collaborative, multidisciplinary work on a research project at the intersection of ethics and digital health for students to synthesize their skills and knowledge acquired during the course; and
  • mentoring for students to write an op-ed on a specific topic in ethics for digital health targeted at the general public.



Mechanisms and Practice (INTC M721)

Vagelos College of Physicians and Surgeons, Columbia University, Spring 2022 and Spring 2021
Course Instructor: Herbert Chase, MD, MA
Role: Preceptor in Clinical Informatics

Medical school clerkships are divided by two week-long intersessions called Mechanisms and Practice in which students come together for classroom-based small-group teaching. This design allows students to take a step back from their clerkships and process their experiences, exploring aspects of medicine and health care that run across the different clerkships, such as understanding the patient-physician relationship and accessing as well as using medical literature. This results in a well-rounded, comprehensive learning experience for students.



Acculturation to Medicine & Clinical Informatics (BINF G4011)

Graduate School of Arts and Sciences, Columbia University, Fall 2020
Course Instructor: Sivan Kinberg, MD, MS, MA
Role: Teaching Assistant

This course offers an introduction to the practice and culture of medicine for informaticians using a mix of lectures, case-based discussions, and critical review of scientific journal articles. The goal is to “acculturate” students without clinical backgrounds to the practice of medicine to inform the study and design of clinical information systems. Each class session is structured to touch upon items from one or more of 3 key competency areas: biomedical science, clinical workflow and culture, and clinical informatics. Students learn about medical language and terminology; basic anatomy and physiology; introductory pathology and pathophysiology; the process of medical decision-making; patient safety, medication safety, and health IT; telemedicine; and the flow of information in the practice of medicine.



Research with Large Databases (HPM 299)

T.H. Chan School of Public Health, Harvard University, Summer 2017 and Summer 2016
Course Instructors: Ellen P. McCarthy, PhD, MPH and John Ayanian, MD, MPP
Role: Teaching Fellow

This course provides an overview of existing large administrative, clinical, and survey databases and addresses the potential uses of these data to study important questions regarding clinical risk factors, treatment, outcomes and health policy. Strengths and limitations of large databases that are commonly used for research are considered, and special attention is devoted to large federal databases that are publicly available and readily usable by new investigators. Students receive hands-on experience using SAS statistical software to obtain, create, manipulate, and analyze large databases. Key statistical issues, including risk-adjustment and sampling weights, is emphasized in the course. Students also evaluate published studies based on large databases and develop a proposal for analyzing a specific research question with a large database.