Health Informatics

Transforming Healthcare Through Data and Information Technology

Our Master’s track in Health Informatics is focused on the application of information technology and data science in healthcare delivery. We study, develop, and improve healthcare information technologies. To apply these information technologies effectively, we also study human and organizational behavior.

What is Health Informatics?

Core Training

Our core curriculum covers three domains:

  • Information technology and data science: Students study statistical methods, machine learning, data management, and informatics standards and technology infrastructure.
  • Health and healthcare: Students learn about domestic and global healthcare, shadow physicians and visit hospitals.
  • Human and organizational behavior: Students cover human factors, human-computer interaction and diffusion of innovation to learn how to position information systems for success.

Health Informatics Venn Diagram

Innovation

Our track offers innovative key informatics skills blended with healthcare system knowledge. Students study cutting-edge topics in health informatics such as statistical and machine learning, data management, and consumer informatics, culminating in a hands-on capstone project with clients from our industry partners.

Collaboration is at the core of our track, with students and faculty from a range of fields, working with a wide array of collaborating NYC institutes. This diversity creates a unique learning environment. To get a sense of our culture, please take a look at our Admissions Information.

Our alumni hold positions in data and policy analysis, health information technology, process improvement, consulting, and more at healthcare institutions and startups. Many alumni pursue doctoral studies.

Students can complete the health informatics curriculum in 12 months.

Research Projects

File Developing a mobile health application to support heart failure symptom monitoring

File Informing, reassuring or alarming? Balancing patient needs in the development of a post-surgical symptom reporting system in cancer

File Methods for integrating EHRs, social determinants of health & built environment data for patient-centered research

File Should parents see teens' medical records? Answers change when people are prompted to think about teen-doctor communication

Prerequisites for Admission

Information Sessions

Program Director

Yiye Zhang, Ph.D., M.S.

HI 1 Year Student - Recommended Curriculum Progression

Students are recommended to follow the schedule below in order to ensure eligibility for graduation. The Education Team will monitor progression, but it is ultimately the student’s responsibility to track their progression to ensure they meet graduation requirements. Course offerings and course availability are subject to change.

Fall Term

Typical course load is 14 credits

Introduction to Biostatistics with STATA Lab (HBDS 5001) OR Biostatistics I with R Lab (HBDS 5005) - Required

Introduction to Biostatistics with STATA Lab
Course Director: Arindam RoyChoudhury, PhD
4 credits

An introduction to the fundamentals of biostatistics with primary emphasis on understanding of statistical concepts behind data analytic principles. This course will be accompanied with a Stata lab to explore, visualize and perform statistical analysis with data. Lectures and discussions will focus on the following: exploratory data analysis; basic concepts of statistics; construction of hypothesis tests and confidence intervals; the development of statistical methods for analyzing data; and development of mathematical models used to relate a response variable to explanatory or descriptive variables.

Biostatistics I with R Lab
Course Director: Xi Kathy Zhou, PhD
4 credits

This course provides an introduction to important topics in biostatistical concepts and reasoning. Specific topics include tools for describing central tendency and variability in data, probability distributions, sampling distributions, estimation, and hypothesis testing. Assignments will involve computation using the R programming language.

Introduction to Health Informatics (HINF 5001) - Required

Course Director: Marianne Sharko MD, MS
3 credits

Health informatics is the body of knowledge that concerns the acquisition, storage, management and use of information in, about and for human health, and the design and management of related information systems to advance the understanding and practice of healthcare, public health, consumer health and biomedical research. The discipline of health informatics sits at the intersection of several fields of research – including health and biomedical science, information and computer science, and sociotechnical and cognitive sciences. In recent years we have witnessed how the collection, storage and usage of digital health data has exponentially grown. Increases in the complexity of health information systems have driven growth in demand for a specialized workforce. This course introduces the field of health informatics and provides students with the basic knowledge and skills to pursue a professional career in this field and apply informatics methods and tools in their health professional practice.

Research Methods in Health Informatics (HINF 5004) - Required

Course Director: Yunyu Xiao, PhD
3 credits

Informatics innovations have their desired impact only when they have high quality, are highly usable, are integrated into their organizational setting, and are widely adopted and used. That makes it critical for informatics students to understand not only how informatics innovations work, but also the users and settings in which they are used. Students will learn methods and models for: measuring data and system quality; assessing and predicting technology adoption (what makes technology sticky?); improving humancomputer interaction via human factors engineering; understanding organizational and systemic challenges in the real world; influencing patients’ health behavior and decisions; and assessing quality, safety, and cost outcomes using health services research study designs. In this mixed methods course, students will gain experience using both quantitative and qualitative methods.

Healthcare Organization and Delivery (HPEC 5002) - Required

Course Director: Lisa Kern MD, MPH
3 credits

The goal of this course is to educate students about the complexity and nuances of healthcare delivery. The course will be especially useful for non-clinicians who intend to go into fields that will require a detailed understanding of healthcare. Class sessions will not summarize healthcare; rather, they will analyze healthcare – through themes such as people, time, money, communication, uncertainty, and others. Students will come away from the course with a deeper appreciation of why it is difficult to change healthcare. They will then be able to anticipate the intended and unintended consequences of interventions and policies that they and others might implement.

Master’s Project 1 and Professional Development (HCPR 9010) - Required

Course Director: Faculty
1 credit

This is the culminating capstone course of all masters-level graduate education programs. It has two aims: (1) helping students to discover and develop new and effective ways of managing and working together with all the stakeholders within the healthcare field and (2) helping accelerate a student's development of 12 the context awareness, integrative management, and industry skills that are needed to lead in a rapidly changing healthcare sector. This capstone course puts students in a new organization, one they don’t already know well, and gives them the chance to practice hitting the ground running. This culminating course provides a deeper preparation for the next stages of a student's career. The capstone project will last the entire year: the first term involves matching students with the right project, the second term has students working with their client, and the third term consists of a detailed report and final presentation in front of the client as well as faculty and fellow classmates.

Spring Term 

Typical course load is 11 credits

Health Information Standards & Interoperability (HINF 5020) - Required

Course Director: Jyoti Pathak, PhD
3 credits

In modern healthcare. exchange of clinical data across multiple stakeholders — between healthcare organizations, between providers and patients, and among agencies and governmental entities — is pivotal. Health information standards provide the “backbone” to achieve uniform data interoperability and exchange across multiple heterogeneous systems. This course will introduce existing and emerging clinical data modeling, terminology and knowledge representation standards.

Master’s Project 2 (HCPR 9020) - Required

Course Director: Faculty
2 credits

This is the culminating capstone course of all masters-level graduate education programs. It has two aims: (1) helping students to discover and develop new and effective ways of managing and working together with all the stakeholders within the healthcare field and (2) helping accelerate a student's development of the context awareness, integrative management, and industry skills that are needed to lead in a rapidly changing healthcare sector. This capstone course puts students in a new organization, one they don’t already know well, and gives them the chance to practice hitting the ground running. This culminating course provides a deeper preparation for the next stages of a student's career. The capstone project will last the entire year: the first term involves matching students with the right project, the second term has students working with their client, and the third term consists of a detailed report and final presentation.

Natural Language Processing (HINF 5016) - Elective

Course Director: Yiye Zhang, PhD
3 credits

This course introduces students to the field of natural language processing (NLP), applied to the health domain. NLP focuses on text data, which lacks the structure of conventional tabular data. In the health domain text is abundant in electronic health records, the medical literature and on the Web. Important applications of NLP include information extraction (pulling facts out of text) and information retrieval (searching through a collection of texts). The course presents methods for working with text: identifying the elements (words and symbols), recognizing sentence boundaries, parsing syntactic structures, assigning meaning, and establishing the structure of the discourse as a whole. The students build skills with these methods through laboratory work.

Artificial Intelligence in Medicine (HINF 5012) - Elective

Course Director: Fei Wang, PhD
3 credits

Introduces students to a variety of analytic methods for health data using computational tools. The course covers topics in data mining, machine learning, classification, clustering and prediction. Students engage in hands-on exercises using a popular collection of data mining algorithms.

Summer Term

Typical course load is 9 credits

Clinical Informatics (HINF 5011) - Required

Course Director: Sameer Malhotra, M.B.B.S., M.A.
3 credits

Prerequisites: Introduction to Health Informatics
Clinical information systems such as electronic health records are central to modern healthcare. This course introduces students to the complex infrastructure of clinical information systems, technologies used to improve healthcare quality and safety (including clinical decision support and electronic ordering), and policies surrounding health information technology.

Data Management (SQL) (HINF 5018) - Required

Course Director: Yiye Zhang, PhD
3 credits

Database systems are central to most organizations’ information systems strategies. At any organizational level, users can expect to have frequent contact with database systems. Therefore, skill in using such systems – understanding their capabilities and limitations, knowing how to access data directly or through technical specialists, knowing how to effectively use the information such systems can provide, and skills in designing new systems and related applications – is a distinct advantage and necessity today. The Relational Database Management System (RDBMS) is one type of database systems that are most often used in healthcare organizations and is the primary focus of this course. An overview of the non-relational database structure will also be given using Python programming language to provide a fuller picture of the current data management landscape. Further, to provide students with opportunities to apply the knowledge they learn from the lectures, various homework assignments, lab assignments, an exam, and a database implementation project will be given.

Master’s Project 3 (HCPR 9030) - Required

Course Director: Faculty
3 credits

This is the culminating capstone course of all masters-level graduate education programs. It has two aims: (1) helping students to discover and develop new and effective ways of managing and working together with all the stakeholders within the healthcare field and (2) helping accelerate a student's development of the context awareness, integrative management, and industry skills that are needed to lead in a rapidly changing healthcare sector. This capstone course puts students in a new organization, one they don’t already know well, and gives them the chance to practice hitting the ground running. This culminating course provides a deeper preparation for the next stages of a student's career. The capstone project will last the entire year: the first term involves matching students with the right project, the second term has students working with their client, and the third term consists of a detailed report and final presentation in front of the client as well as faculty and fellow classmates.

Health Behavior and Consumer Informatics (HINF 5017) - Elective

Course Director: Faculty
3 credits

Consumer health informatics (CHI) is the study of consumer information needs and technologies that provide consumers with the information they need to be more engaged in self-care and healthcare. This introductory CHI course will present an overview of theories of health and information behavior; key concepts and terminology; and main application domains. We will explore how health behavior theories 8 provide a framework for explaining consumers’ health behaviors and how CHI tools that are built with a theoretical foundation can promote health behavior change. The course will cover CHI applications in major application domains including electronic patient portals, mobile health (mHealth), and telehealth. Students will learn how to assess end-user needs and technological practices of potential users who experience health information and technological disparities. Students will also learn how to design for endusers, evaluate CHI applications and research.