Skip to main content
Biomedical Informatics Biomedical Informatics
  1. HOME
  2. Education & Training
  3. Courses
  4. FMPH431

FMPH 431: Special Topics in Public Health: Public Health Informatics (4 credits)

Instructor: Michael Hogarth, M.D.

When and where: Offered Winter 2023 Quarter

Overview: The course will introduce the students to the discipline of public health informatics.  Students will learn informatics concepts, techniques and systems that support public health practice. The course explores public health information systems including immunization information systems (IIS), disease surveillance systems (i.e., BioSense), disease reporting (ELR), registries (i.e., cancer registries), outbreak management (case investigation/contact tracing), and vital statistics.  Students are exposed to prevailing data standards (PHIN, HL7, IHE), data representation (biomedical terminologies), and interoperability with electronic health record systems (EHRs).  The course introduces the student to basic data management skills and concepts through demonstrations and hands-on lab exercises.

Objectives
Upon completion of this course, students will be able to:

  • Define the field of public health informatics and its relationship with biomedical informatics, computer science, and data science
  • Describe how informatics is used to support the 10 essential services of public health.
  • Define key principles of computation on data, including data representation, database models, transactional vs. analytical operations, natural language processing, computational models, and data representation.
  • List and describe public health information systems (immunization system, surveillance, registries, electronic lab reporting, vital records, etc..)
  • Perform SQL-based querying of a real (de-identified) clinical data set in a relational database and use the results for statistical analysis.
  • Create geo-spatial visualizations using a GIS tool and public health data from the California Open Data portal.
  • Describe how emerging technologies (mobile apps, voice systems, sensors/wearables) can be used in disease surveillance and pandemic response. 

Class organization:  In class lecture, hands-on practice, discussion and student presentation

Topics

  • Database (data persistence) information models
  • Standardized health terminologies and classifications
  • Application ontology building
  • Graph information system standards (SPARQL)

Materials

  • Class reading materials will be distributed before each session as necessary
  • A laptop computer will be required for the course

Textbook
Public Health Informatics and Information Systems, 3rd edition. Magnuson and Dixon. 2020.  Available free for UCSD students through Springer eLink:
https://link.springer.com/book/10.1007/978-3-030-41215-9

Grading
  • Course Project: 40%
  • Lab Exercises (6 labs @10% each: NLP, RDBMS 1, RDBMS 2, RDBMS 3, GIS1, GIS2 ): 60%

Course Components

  • Lectures (29 lectures) – MWF (1hr) x 10 weeks. NO CLASS Memorial Day (Monday, May 29th)
  • Permission to access the MIMIC data set (requires CITI training, instructions provided in the course)
  • Applied lab exercises (6 hands-on guided labs) – performed as homework assignments for credit
  • Course Project – A Master Data Management Plan for a proposed 21st century (next generation) Biosurveillance System