Curriculum

Week 1: Setting the Stage: Understanding the AI Landscape in Health Systems

This week introduces learners to the structure and goals of the Certificate in Applied AI for Health Systems and provides an essential grounding in the competencies required to design and implement real‑world AI solutions. Through a case study of an AI tool designed to detect domestic violence using data in the electronic health record (EHR), students learn the full lifecycle of deploying AI in healthcare, from problem identification and data governance to workflow integration, oversight and evaluation. The week then zooms out to explore the historical and technological forces that led to the modern AI era, including exponential growth in computing, Moore's Law, diffusion-of-innovation patterns and the Gartner Hype Cycle. Learners examine why AI is becoming central to achieving the Triple Aim and preview emerging use cases poised to reshape clinical and operational care. By the end of the week, students have a clear roadmap of the certificate, a shared conceptual vocabulary and a big-picture understanding of why AI matters for the future of health systems.

Week 2: Foundations of Digital Health and AI

This week provides a clear conceptual and technical foundation for understanding digital health and AI. Learners begin with a historical overview of how electronic systems, consumer technologies, big data and early machine learning paved the way for today's AI landscape, establishing key terminology, milestones and cultural dynamics that shape adoption. The week then introduces the core building blocks of modern AI, including neural networks, machine learning, deep learning and generative AI, as well as concepts such as bias, inference and prompt engineering. Students also learn how large language models generate outputs and why hallucinations occur, along with how retrieval‑augmented generation (RAG) helps mitigate them. By the end of the week, learners have the vocabulary and foundational understanding needed to interpret AI systems and engage meaningfully with the technical content that follows in later weeks.

Week 3: Health Data and Analytics Foundations

This week introduces students to the core principles of health data and analytics, the backbone of all AI systems in modern healthcare. Through real‑world case examples, learners explore major healthcare data sources, the informatics pyramid, the "Four Vs" of big data and the strengths and limits of analytic methods such as descriptive, predictive and prescriptive analytics. The week also covers common pitfalls in algorithmic performance, including data quality issues and the "garbage in, garbage out" problem. Students then gain a practical foundation in data types and visualization theory, learning how to present health information clearly and how effective visualizations support insight, interpretation and trust in AI‑enabled environments. By the end of the week, learners understand how raw data becomes meaningful information and how to evaluate it responsibly in the context of AI.

Week 4: AI Use Cases I: Clinical and Operational Care

This week provides a grounded, practical tour through consequential applications of AI in healthcare: diagnostics, clinical decision support, virtual triage, ambient documentation, workflow optimization and operational efficiency. Building on the foundations from earlier weeks, learners examine how AI tools actually perform in real‑world environments: where they succeed, where they fail and what determines whether they meaningfully improve care. Through case studies of high-profile failures and emerging innovations, students analyze how data quality, workflow integration, human oversight, regulatory frameworks and ROI influence AI's impact. The week also introduces a structured method—SWOT analysis—for evaluating AI solutions and identifying remaining unmet needs that future tools may address.

Week 5: AI Use Cases II: Digital Health Platforms and Remote Care Technologies

This week introduces the digital health infrastructure that enables modern remote care, focusing on mobile health (mHealth), wearable biosensors and remote patient monitoring (RPM). Students learn how smartphones, sensors and connected devices generate continuous streams of patient data and how these data support clinical insight, engagement and learning health system improvement. The week also covers the EHR as the central platform for storing, exchanging and integrating digital‑health data, emphasizing interoperability, APIs, standards like FHIR and the practical challenges of connecting tools to clinical workflows. By examining data flows, usability barriers and real-world implementation issues, learners gain a practical understanding of the "plumbing" required to make digital tools effective and prepare the ground for later AI‑enabled applications.

Week 6: Leadership and Change Management in the AI Era

This week focuses on the leadership competencies, strategic decision‑making frameworks and cultural change mechanisms required to guide AI transformation in modern health systems. AI adoption is not merely a technical upgrade, it represents an organizational, cultural and operational shift that requires leaders to navigate uncertainty, build trust, align stakeholders and foster psychological safety across diverse clinical and administrative teams. Through case studies of successful AI deployments and high-profile failures, learners explore what distinguishes organizations that achieve AI-enabled transformation from those that stall in pilot purgatory. The week also examines evidence-based change management models and the leadership behaviors necessary to translate AI initiatives into sustained value, operational improvement and workforce engagement.

Week 7: Principles of AI Research in Health Systems Science

This week introduces students to the foundations of research design in health system science and then connects those foundations to the practical use of AI across the research lifecycle. The first lecture focuses on core concepts in research methods, including how to move from a real‑world problem to a focused research question or hypothesis, how to structure questions using frameworks such as PICO/PICOTS and when to use different quantitative, qualitative and mixed-methods designs (for example, observational studies, randomized trials, real-world evidence, implementation research and systematic reviews). The second lecture examines how AI methods such as natural language processing and machine learning are being integrated into each stage of research, from patient identification and recruitment through data capture, analysis and reporting, with examples mapped to the study designs introduced earlier in the week. Throughout, students are introduced to research-specific ethical and data privacy considerations, potential new sources of bias introduced by AI and practical strategies to mitigate these risks, preparing them to evaluate and design AI-enhanced studies within health systems.

Week 8: Design Thinking for AI in Health Systems

This module introduces learners to design thinking as a structured framework for innovation. Design thinking is a user‑centered process that is used to design, develop, implement, test and market AI and digital solutions to healthcare challenges. In this week, you will learn how design thinking emerged, some notable successes such as Apple, what the stages are and how to follow them. These steps will be the basis of the Capstone project, which will leverage the design thinking process to develop solutions to unsolved health challenges using AI technologies.

Week 9: AI Coding Studio I: Vibe Coding Foundations

Go from idea to execution without writing a single line of code. In this hands‑on studio, you will master "vibe coding": the ability to architect complex software behaviors using natural language prompts. We will explore high‑impact workflows using industry-standard tools (GPT Codex, Claude Code, Gemini) and demystify technical concepts like model selection and Git version control. By the end of this module, you will have applied iterative logic chains to real-world problems.

Week 10: AI Coding Studio II: Hands‑On with No-Code Development

Transition from basic prompts to rapid deployment. In this intensive studio, you will leverage vibe coding to architect three distinct project types in real‑time: a workflow automation script, a responsive web application and a functional iOS mobile app. Beyond simple generation, this session emphasizes "production readiness": teaching you how to implement security best practices, handle complex edge cases and master iterative debugging strategies to overcome common AI errors and workflow bottlenecks.

Week 11: AI Ethics, Equity and Safety

This week provides a critical exploration of the ethical landscape surrounding the use of AI in healthcare. Students will examine the foundations of digital health and AI, the core ethical principles that guide responsible innovation and the real‑world risks that arise when these principles are not upheld. Through case examples, regulatory context and best-practice frameworks, the week emphasizes how issues such as bias, privacy, transparency and accountability shape the safe and equitable use of AI across clinical and operational settings. Learners will also engage with practical strategies for governance, oversight and trustworthy implementation, preparing them to identify ethical challenges and support responsible AI adoption within their own organizations.

Week 12: Capstone Project Presentations

Students will develop and deliver a recorded seven‑minute presentation that demonstrates their healthcare innovation project from problem identification through solution development. The presentation will showcase the student's design thinking process, competitive analysis, solution ideation and AI-assisted development ("vibe coding") or high‑fidelity prototyping efforts.