Course 2025-2026 a.y.

The Data Systems in Healthcare course offers an in-depth exploration of how health systems generate, structure, and leverage data to drive decision-making, innovation, and policy transformation. At the crossroads of health management, data science, and digital innovation, the course equips students with the analytical and critical tools needed to understand and shape the data ecosystems underpinning modern healthcare.

Through a blend of conceptual frameworks, case-based discussions, and hands-on simulations, students will learn how data flow across hospitals, regional systems, and national infrastructures, and how governance models, interoperability standards, and regulatory frameworks influence their use. Real-world examples will illustrate how data—from clinical records and administrative databases to imaging, genomic, and digital health data—can be harnessed to support clinical research, improve patient outcomes, and inform policy choices.

By the end of the course, students will be able to navigate the complexity of healthcare data systems with both technical literacy and strategic awareness, understanding how data governance, ethics, and AI applications can jointly shape the future of sustainable healthcare.

• Understand the organization and functioning of healthcare systems, including their data flows, regulatory principles, and governance structures.
• Recognize the different types of health data — clinical, administrative, epidemiological, imaging, and omics — and their relevance for research, management, and policy design.
• Analyze how data systems and digital infrastructures support healthcare delivery, performance evaluation, and innovation in clinical and policy contexts.
• Evaluate key challenges in health data governance, including privacy, interoperability, ethical considerations, and the regulatory frameworks that shape data use and sharing.
• Apply conceptual frameworks to assess how data and information systems can enhance evidence-based decision-making and the overall efficiency of healthcare systems
• Develop actionable recommendations for data governance and policy design in realistic healthcare scenarios.

• Analyze how healthcare data flow across organizations and how system design, governance, and regulation shape their use.

• Evaluate the strengths, limitations, and biases of different types of health data (e.g., clinical, administrative, imaging, digital).

• Assess the implications of data governance, privacy, and interoperability for real-world decision-making in healthcare.

• Apply economic evaluation methods (e.g., CEA, CBA, HTA) to compare health technologies and inform policy or managerial choices.

• Interpret measures of value—such as QALYs, patient preferences, wellbeing indicators—and use them to support evidence-based recommendations.

• Construct simplified decision models (e.g., decision trees, Markov models, budget impact analyses) to forecast health and economic outcomes.

• Collaborate effectively in teams to solve simulated policy or management challenges, integrating diverse perspectives and responsibilities.

• Communicate complex data insights clearly and persuasively in presentations, discussions, and written assignments.

• Reflect critically on the societal, ethical, and organizational implications of data use in healthcare systems.

• Lectures
• Guest speaker's talks (in class or in distance)
• Practical Exercises
• Individual works / Assignments
• Collaborative Works / Assignments
• Interaction/Gamification

The course combines theoretical lectures with interactive and experiential learning activities, encouraging students to connect the concepts of data systems to real-world healthcare contexts.

The course is grounded in an interdisciplinary and reflective learning philosophy. Students are invited to connect technical understanding with organizational reasoning, learning not only how health data are managed, but also why these processes matter for patients, institutions, and society. The classroom is conceived as a collaborative lab where learning happens through dialogue, simulation, and critical inquiry rather than passive note-taking.

Teaching methods include:

• Interactive lectures introducing key frameworks on healthcare systems, data governance, and digital infrastructures.
• In-class discussions and simulations, where students assume the roles of decision-makers (e.g., policymakers, data officers, clinicians) addressing realistic challenges.
• Case studies exploring data-driven innovations, governance dilemmas, and the impact of AI applications in healthcare systems.
• Mini-labs and demonstrations illustrating how health data are structured, accessed, and interpreted through simple, real or simulated examples.
• Reflective and peer-learning activities, such as short written briefs, oral commentaries, or guided debates connecting readings and practice.
• Guest lectures from professionals and scholars in health data management, AI in medicine, and policy innovation, to bridge academic and professional perspectives.

Active participation is a fundamental component of the course. Students are expected to prepare assigned readings, contribute to discussions, collaborate in group activities, and engage in reflective learning exercises throughout the course.

Assessment is designed to evaluate not only knowledge acquisition but also the ability to integrate, apply, and critically reflect on the concepts covered in the course. The evaluation combines continuous assessment with a structured, case-based final exam to ensure both engagement and analytical depth.

Written exam (made of 10 multiple choices in 40 minutes) on all reading materials as specified in the syllabus and made available in the e-learning.

There is no textbook for attending students. All the reading materials will be made available. For detailed instructions refer to the syllabus.