Overview

Welcome to DH 302 - Introduction to Public Health Informatics! The course focuses on applying informatics principles and computational methods to address public health challenges and improve population health outcomes.

About the course

We will take a comprehensive approach to understanding how informatics tools and techniques can be applied to solve real-world public health problems. The course will build your foundational knowledge and skills necessary to analyze health data, design health information systems, and evaluate the impact of informatics interventions.

The course will emphasize practical applications using real health datasets and case studies from public health practice. We will explore multiple domains including disease surveillance, health promotion, healthcare delivery, and policy evaluation.

The course is designed for students with diverse backgrounds who want to learn how computation and data science can improve public health practice.

What will you learn?

Public health informatics combines the principles of computer science, information science, and public health to improve health outcomes at the population level. Overall, the field addresses critical challenges in disease prevention, health promotion, and healthcare delivery.

You will learn to work with various types of health data including electronic health records, surveillance data, survey data, and administrative datasets. The course will cover statistical methods, data visualization techniques, and computational approaches specifically tailored for public health applications.

A detailed set of topics will be available here. But overall, you will learn about:

  • Introduction to public health informatics and health information systems
  • Health data standards and interoperability
  • Electronic health records and their applications in public health
  • Disease surveillance systems and outbreak detection
  • Statistical methods for health data
  • Data visualization and dashboard development for public health
  • Geographic information systems (GIS) in public health
  • Health information exchange and data sharing
  • Emerging technologies in public health (AI, machine learning, mobile health)
Learning objectives

  • Understand the principles and applications of informatics in public health practice

  • Analyze and visualize health data to identify trends and patterns relevant to population health

  • Design and evaluate health information systems and interventions

  • Apply statistical and computational methods to address public health questions

  • Develop skills in data management, analysis, and presentation for public health audiences

  • Write reproducible, well-documented code for health data analysis

Evaluation

The course will be evaluated based on the following components:

Upto Midsem (50%):

  • Assignments: 10%
    • Due on Fridays 6pm via Gradescope
    • Best n-1 of n
    • Late submission policy: 10% penalty per day upto a maxium of 3 days
  • Mid-sem: 30%
    • Closed book and offline
  • Surprise quizzes: 10%
  • Class participation: Bonus points

After Midsem (50%): - Final exam: 30% - Closed book and offline - Course project: 20% - Group project in teams of 3-4 students - Project proposal due by end of week 6 - Final project report and presentation due by end of week 14

Collaboration policy

For assignment problems, you should work on your own. If you get stuck, you are welcome to discuss it with other students (in-person, or online on Piazza). However, the solutions must be your work. If you discussed with someone, please mention their name and what you received help with in your submission.

Mid-semester exams will be closed-book. No collaboration is allowed.

For the course project, you will work in groups of 3-4 students. All group members are expected to contribute equally to the project.

LLM Policy

You are allowed to use Large Language Models (LLMs) like ChatGPT, Claude, etc. as learning aids, but you must:

  1. Clearly document when and how you used an LLM in your submission
  2. Ensure you understand the solutions provided by the LLM
  3. Be prepared to explain your work during office hours or exams
  4. Not rely solely on LLM-generated code without understanding

For exams, LLMs will not be permitted.

Course information

Units: 6

Lecture: Wednesdays and Fridays, 11:05am – 12:30pm.

Location: LC TBA, TBA Floor Lecture Hall Complex, L1 Building, Opp. KReSIT Bldg., Between Physics & MEMS Dept. GMaps coordinates

Instructors: - Saket Choudhary | Homepage | Blog, B-22, KCDH, KReSIT Basement - Nirmal Punjabi, CC-110, New Computer Science Building

Office Hours:

Teaching Assistants:

TBA