MS in Health Informatics Curriculum & Specializations - Northwestern School of Professional Studies

The course provides knowledge of the key components of health care in the United States—the policy, economic, and societal forces that shape health care delivery. The course serves as an introduction to elements of the American health care system, including the provider components, the financing of health care, the basic structure of public policy making and public health systems, a comparative analysis of the American system to health care systems of other countries, and the legal and regulatory framework within the American health care system functions. In addition to the structural components of the system, the course reviews current issues within the American health care system, including public health, preparedness, quality of health care, health reform, payment mechanisms, and consumerism.

Provides insight into the clinical care process. Designed for students not previously involved in clinical medicine as a nurse, pharmacist, or physician, as well as those trained in medicine outside the U.S. Includes basic medical terminology and introductory psychophysiology. Topics include eliciting information from patients, synthesizing history and physical examination, decision making for ordering tests, establishing diagnoses, treatment planning, integrating evidence-based medicine, and using an intelligent medical record in a complex environment.

The course is an introductory survey of fundamentals of health information technology. Topics center on how information technology enables patient care, how information technology is used by healthcare providers and caregivers, and it’s use to fuel modern health care organizations. This course provides an overview of health informatics with emphasis on the factors that helped create and sustain this new field, the key players involved, and the impact health information technology is having on the delivery of care in a rapidly changing healthcare marketplace.

We explore a range of critical health care informatics topics, including: electronic health records, health information exchange, how health information technology impacts quality of care and patient safety, big data and predictive analytics, clinical decision support and knowledge management, regulatory issues, consumerism and technology, systems integration, and virtual health. The course also explores emerging and new uses of technology.

The American health care landscape is incredibly dynamic, rapidly evolving, and highly regulated. The content, research, and group discussions in this course explore these intersections, providing informaticists with knowledge to navigate competing interests, ethical principles, and key regulatory requirements. Key topics include privacy laws and regulations such as HIPAA, GDPR, the 21st Century Cures Act, TEFCA, and emerging state-level laws like the My Health My Data Acts. Students will also examine the ethical challenges of deploying AI in healthcare, including algorithmic bias, fairness in machine learning models, and responsible AI deployment. The course integrates changing financing paradigms, re-imagined health care services delivery systems, and the tension between precision medicine and population health. In addition, students will explore how evolving consumer expectations in the digital age are reshaping healthcare. The challenges of safeguarding individual privacy rights and data security while highlighting the potential of public-private partnerships in advancing health informatics within the "Learning Health Care System." Through research, case studies, and group discussions, students will gain a comprehensive understanding of the regulatory, ethical, and technological forces driving the future of healthcare.

A practical course on acquiring and assessing new medical technology, either as a vendor who needs to know how to meet the expectations of customers and their acquisition requirements or as a customer/practitioner who must know how to validate technology selections and implementations. Topics include cost analysis and justification, economic models, capital purchase, leasing strategies, the application service provider or risk-sharing model, purchase agreements and contracts, writing a RFP, analyzing a RFP response, and industry business trends.

This foundational course introduces Master of Health Informatics students to core principles of artificial intelligence (AI) and data analytics within healthcare. Designed explicitly for health informaticists, rather than technical data scientists, the course emphasizes conceptual understanding and practical applications of AI-driven tools and analytics in healthcare settings. Students will learn to effectively communicate and collaborate with healthcare data analysts, understand roles and workflows related to analytics projects, and develop essential algorithmic thinking skills critical for clinical informatics practice and AMIA board certification. Key topics include predictive analytics, clinical decision support systems, AI-driven patient care innovations, ethical considerations, and navigating regulatory landscapes in healthcare analytics. By completing this course, students will gain essential AI literacy, enabling them to strategically leverage AI and analytics solutions to enhance patient outcomes and healthcare efficiency.

In this course, we will introduce you to the emerging practice area of Consumer eHealth, the aim of which is to empower consumers to better manage and influence their health and wellness, access healthcare services, and improve interactions with their caregivers by leveraging digital health solutions and services. Topics include solutions that emphasize the consumer experience (CX), new consumer access models and modalities, consumer-oriented technologies and systems such as APPs and health and wellness devices and platforms, HIPAA-compliant cloud based services, the use of innovative wearables (i.e., electronic tattoos), internables/ingestibles and consumables, and behavioral management solutions such a Digiceuticals and PHRs. Additional topics include patient generated healthcare data (PGHD) and the evolution of consumer driven healthcare in the United States: specifically, evaluating how a connected society will enable previously unattainable levels of patient/provider inter-activity.

This interdisciplinary course provides students with the knowledge and leadership skills to drive operational excellence and digital transformation in healthcare organizations. Integrating perspectives from both Health Analytics and Health Informatics, the course explores how healthcare systems operate—and how strategy, innovation, data, and AI can be leveraged to improve clinical, operational, and research outcomes.

Students will examine foundational aspects of healthcare operations including governance, interdisciplinary care models, financial stewardship, emergency preparedness, cybersecurity, and quality improvement. In parallel, they will explore the leadership and strategic dimensions of digital health and analytics—developing competencies in business intelligence (BI), systems/data governance, organizational readiness, and stakeholder engagement.

Additional emphasis is placed on identifying opportunities to apply analytics, informatics, and emerging technologies (such as AI, LLMs, and cloud-based tools) to solve real-world challenges. Students will learn how to lead cross-functional teams, apply project and change management tools, and translate complex data into actionable insights. Case-based projects will help students explore how to align innovation and analytics strategies with organizational goals.

MHI 498-DL Capstone Project

As a culminating experience, students will put into practice the knowledge and skills they have learned during their coursework through a Capstone Project. Students will have the opportunity to develop and implement a Health Informatics project with an industry or university partner or in their workplace. Alternatively, students can develop a culminating, two-part project. This alternative capstone project will leverage health informatics to provide an innovative, consultative response to a need or problem arising as part of a real-world case study. The project will challenge each student to conduct and integrate comprehensive research and to apply knowledge, skills, and competencies built through coursework they have completed in the MHI program.

In addition to each student’s individual research and project development, the course emphasizes collaboration with fellow students by using the Canvas discussion board to crowdsource strategies and approaches for their Capstone Project. Each student will work with the instructor to establish an “Advisory Committee” for their project which, ideally, will be comprised of a “Knowledge Expert” from the organization they are working with and a faculty advisor from the Northwestern University Health Informatics program. Prerequisite: The earliest students may take Capstone is in the quarter of their final MHI course in the program.
Note: Registration for this course will close one week prior to the start of the term.

MHI 590-DL Thesis Research 

The 590 Thesis Research is an individual research project in an independent study format. The paper is written under the supervision of an approved faculty member and presents an opportunity to research and explore a topic thoroughly. The typical time to complete the master’s thesis is four months to a year. Please contact your advisor for information on how to register for the MHI Thesis Research course.

This course provides concepts and practical examples of health care information interoperability, standard terminologies, messaging standards, health information exchanges (HIEs), and projects deploying these capabilities. Topics covered by the course include the importance of standards; information architecture and application programming interfaces (APIs); principles and examples of standard terminologies; current messaging standards; and their use in health information exchanges for coordination of care and payment reform. Core principles, challenges, benefits, and limitations will be discussed in each of these topics.

This course provides an introduction to decision support systems in health information technology. Foundational topics covered include decision analysis steps, decision support standards, and application areas such as precision medicine, public/population health, and predictive analytics. Knowledge generation techniques based on evidence-based guidelines, performance measurement, and artificial intelligence (AI) will be presented. A framework for designing and implementing decision support systems will be applied in a final project that outlines a development, implementation, and evaluation plan for a decision support system.

Students may select a third course from the General Health Informatics Track.

This course provides a practical, interdisciplinary exploration of applied artificial intelligence (AI) in healthcare, with a focus on high-impact use cases such as Natural Language Processing (NLP), computer vision, and clinical decision support systems (CDSS). Students will examine real-world applications ranging from automated clinical documentation and radiology image analysis to predictive risk modeling and AI-assisted care delivery.

Designed for healthcare and analytics professionals, this course emphasizes how to evaluate, implement, and lead AI-driven solutions, rather than build them from scratch. Through hands-on case studies and interactive scenario planning, students will learn how to assess data readiness, address model limitations, and communicate the value of AI tools to clinical and operational stakeholders.

Key topics include AI adoption frameworks, handling messy healthcare data, evaluating vendor tools, understanding ethical and regulatory considerations (e.g., HIPAA, bias mitigation, FDA guidance), and aligning AI innovation with organizational goals. By the end of the course, students will be equipped to bridge the gap between advanced AI technologies and responsible, outcome-driven implementation in healthcare settings.

As artificial intelligence (AI) becomes foundational to modern healthcare and biomedical research, scalable infrastructure and cloud computing environments are critical to support real-time analytics, machine learning pipelines, and growing healthcare datasets. This course introduces the key concepts, tools, and architectures that underpin AI-readiness at scale, with a focus on practical applications in health systems, research institutions, and AI innovation hubs.

Students will gain hands-on exposure to core components of modern data infrastructure, including cloud service models (IaaS, PaaS, SaaS), containerization (Docker, Kubernetes), data pipelines, and storage strategies optimized for protected health information (PHI). The course also explores the role of distributed computing, high-performance environments, and MLOps practices for deploying AI models into production.

Through real-world case studies and guided design exercises, students will assess trade-offs in cost, compliance, scalability, and performance, developing a systems-thinking approach to supporting AI and data science initiatives in clinical, operational, and research settings.

In this course, you will be introduced to the design and adoption of AI-enabled digital twinning, the aim of which is to curate dynamic, continuously learning service-oriented solutions that mirror a person’s physiology, behavior, and contextual care. Students will integrate multimodal sensing, machine-learning, and Agentic AI to (1) model functional states such as activity, emotion, and circadian rhythm; (2) generate holistic, outcomes-based care plans; (3) develop prescriptive “clinical sandboxes” that predict risk and compare treatment pathways (i.e., sepsis, mortality, and outcomes) inclusive of social determinants of health; and (4) address data-governance challenges of consent, provenance, and portability. By the end of the course, learners build a functioning Digital Twin Agentic AI Agent service that uses a blend of language and/or vision models based on a digital-twin dataset of their choice.

This course provides concepts and practical examples of health care information interoperability, standard terminologies, messaging standards, health information exchanges (HIEs), and projects deploying these capabilities. Topics covered by the course include the importance of standards; information architecture and application programming interfaces (APIs); principles and examples of standard terminologies; current messaging standards; and their use in health information exchanges for coordination of care and payment reform. Core principles, challenges, benefits, and limitations will be discussed in each of these topics.

This course provides an introduction to decision support systems in health information technology. Foundational topics covered include decision analysis steps, decision support standards, and application areas such as precision medicine, public/population health, and predictive analytics. Knowledge generation techniques based on evidence-based guidelines, performance measurement, and artificial intelligence (AI) will be presented. A framework for designing and implementing decision support systems will be applied in a final project that outlines a development, implementation, and evaluation plan for a decision support system.

This course provides a practical, interdisciplinary exploration of applied artificial intelligence (AI) in healthcare, with a focus on high-impact use cases such as Natural Language Processing (NLP), computer vision, and clinical decision support systems (CDSS). Students will examine real-world applications ranging from automated clinical documentation and radiology image analysis to predictive risk modeling and AI-assisted care delivery.

Designed for healthcare and analytics professionals, this course emphasizes how to evaluate, implement, and lead AI-driven solutions, rather than build them from scratch. Through hands-on case studies and interactive scenario planning, students will learn how to assess data readiness, address model limitations, and communicate the value of AI tools to clinical and operational stakeholders.

Key topics include AI adoption frameworks, handling messy healthcare data, evaluating vendor tools, understanding ethical and regulatory considerations (e.g., HIPAA, bias mitigation, FDA guidance), and aligning AI innovation with organizational goals. By the end of the course, students will be equipped to bridge the gap between advanced AI technologies and responsible, outcome-driven implementation in healthcare settings.

As artificial intelligence (AI) becomes foundational to modern healthcare and biomedical research, scalable infrastructure and cloud computing environments are critical to support real-time analytics, machine learning pipelines, and growing healthcare datasets. This course introduces the key concepts, tools, and architectures that underpin AI-readiness at scale, with a focus on practical applications in health systems, research institutions, and AI innovation hubs.

Students will gain hands-on exposure to core components of modern data infrastructure, including cloud service models (IaaS, PaaS, SaaS), containerization (Docker, Kubernetes), data pipelines, and storage strategies optimized for protected health information (PHI). The course also explores the role of distributed computing, high-performance environments, and MLOps practices for deploying AI models into production.

Through real-world case studies and guided design exercises, students will assess trade-offs in cost, compliance, scalability, and performance, developing a systems-thinking approach to supporting AI and data science initiatives in clinical, operational, and research settings.

In this course, you will be introduced to the design and adoption of AI-enabled digital twinning, the aim of which is to curate dynamic, continuously learning service-oriented solutions that mirror a person’s physiology, behavior, and contextual care. Students will integrate multimodal sensing, machine-learning, and Agentic AI to (1) model functional states such as activity, emotion, and circadian rhythm; (2) generate holistic, outcomes-based care plans; (3) develop prescriptive “clinical sandboxes” that predict risk and compare treatment pathways (i.e., sepsis, mortality, and outcomes) inclusive of social determinants of health; and (4) address data-governance challenges of consent, provenance, and portability. By the end of the course, learners build a functioning Digital Twin Agentic AI Agent service that uses a blend of language and/or vision models based on a digital-twin dataset of their choice.

Students will learn about current and future data trends, relational databases, healthcare data standards, and the basics of utilizing SQL for data and analytics including the use of GenAI. Becoming familiar with the fundamentals of relational databases and SQL, the most popular language used to query data from relational databases, is an essential part of this class.

Students will leave this course with a strong understanding of standard healthcare terminologies (i.e., RxNorm, SNOMED), relational databases, and how to retrieve, analyze, and aggregate relational data for analytics purposes using SQL. Concepts learned in this class will also introduce students to deeper data modeling concepts(OMOP, FHIR).

This is an introductory course to general concepts and fundamentals in the practice of biostatistics as commonly used in health data. This will form the foundation for more advanced topics to come in later courses. The material covered will be sufficient to perform basic descriptive statistical analyses.

The goal of this course is to teach students techniques in how to perform statistical analysis of health data sets in R and Python. The techniques you’ll learn in this course are important in themselves, and will form the foundation for later courses in the MHDS program.

Prerequisite: MHDS 401 Foundation of Healthcare Programming with Python and R

This course covers the fundamentals of database design and management. Topics include the principles and methodologies of database design, database application development, normalization, referential integrity, security, relational database models, and database languages. Principles are applied by performing written assignments and a project using an SQL database system.

Data and Digital platforms are key investments that help companies gain competitive edge by enabling new digital business models and improving enterprise business performance. In this course students will gain hands-on experience in the implementation of Data and Digital platforms by leveraging public cloud and emerging technologies (e.g., big data technologies, AI/ML, APIs, digital twin, and IoT). This course will also prepare students to design and deliver enterprise scale digital transformation initiatives. (Required: CIS 414-DL, MSDS 430-0, or MSDS 430-DL. Recommended: CIS 417-0 or CIS 417-DL and CIS 435-0 or CIS 435-DL.)

Overview of the structure of the U.S. health systems followed by a selective international comparison of other health delivery systems including their relationships to social policies and economic factors.

This is an introductory course positioning technology in the global health landscape. Health systems of the future will increasingly be dependent on technology; how the technology value-proposition is leveraged will be a critical determinant of health outcomes, nowhere more so than in developing countries and resource-scarce settings (DC&RSS). Topics will include: health technology - what’s in a name (unpacking the term); why health technologies matter (linking technologies to burden of disease, healthcare services, quality of care and health outcomes); health technology innovation, introduction/adoption and utilization challenges in DC&RSS; the complementary roles of health technology assessment, regulation and management; health-related technologies and infrastructure as the new frontier for achievement of improved health status in DC&RSS.

The User-Centered Design course gives students hands-on experience with the latest design frameworks and methodologies that focus on the end user. Students will learn how a user focused design process can be used to solve the most challenging problems facing businesses and organizations today. Students will be introduced to the latest trends in design thinking, the importance of iterative design frameworks, researching user needs, prototyping, collaboration and critical feedback.

In this course, students will explore the foundational pillars for the Information Design and Strategy program. The course will coalesce techniques for using data to inform design & strategy within the business or professional setting. Through hands-on assignments and activities, students will gain best practices and walkway with a toolbox of knowledge to succeed in the real world. This course is highly recommended to be taken as a first or early course in the MS IDS program.

Students learn to apply statistical techniques to the processing and interpretation of data from various industries and disciplines. This course introduces statistical models as they are used in predictive analytics. It addresses issues of statistical model specification and model selection, as well as best practices in developing models for management.

This course introduces best practices in project management, covering the full project life cycle with a focus on globally accepted standards. The course introduces traditional/waterfall, hybrid, and iterative/agile approaches to project management. Regarding traditional methods, the course reviews project integration management, portfolio and stakeholder management, chartering, scope definition, estimation, precedence diagrams, and the critical path method. It also reviews scheduling, risk analysis and management, resource loading and leveling, Gantt charts, earned value analysis and performance indices for project cost and schedule control. By applying methods discussed in this course, students will be able to execute information systems and data science projects more effectively.