Digital technologies are reshaping healthcare—advances in hardware and software are enabling more personalised prevention, more accurate diagnoses, and therapies tailored to individual patients. These developments promise better health outcomes and, over time, lower costs. The English-language Master's programme in Digital Health is built to prepare graduates for this changing landscape by combining technical and medical perspectives.
This interdisciplinary MSc (four semesters) is designed for students with backgrounds in computer science or medicine who want to work at the intersection of IT, computer science, and clinical practice. Delivered by the Faculty of Digital Engineering—a joint faculty established by the Hasso Plattner Institute (HPI) and the University of Potsdam and based in Bergholz‑Rehbrücke—the course covers core topics in IT systems engineering and data engineering alongside essential medical foundations and comparative healthcare-system knowledge. Students learn to analyse, design and implement complex, secure IT systems and infrastructures for healthcare, and to address the ethical and legal issues involved in health‑system design. The HPI also emphasizes interdisciplinary soft skills, entrepreneurship and design‑thinking methods that are important for leading large, networked IT projects.
Key facts / entry profile
This full-time Master’s curriculum is worth 120 ECTS and is organised to combine core knowledge, in-depth specialisation, practical experience and a substantial research thesis. The programme dedicates roughly a third of the workload to required courses that establish the foundations of digital health, a further third to elective specialisation modules, and the remainder to practical and research components (soft skills, a hands-on project lab and the Master’s thesis).
Students will develop both technical and research competencies: core Digital Health modules build domain-specific expertise, elective specialisations allow focused study across two chosen areas, and the digital health project lab gives team-based, research-linked experience developing new solutions. The 30‑credit Master’s thesis consolidates independent scholarly work and is a major part of the degree. Soft skills training and a bridge/compulsory elective module ensure graduates are prepared to communicate, collaborate and transition from prior qualifications or different academic backgrounds.
Key features and learning outcomes
Program requirements (summary)
This master’s program requires applicants to demonstrate both appropriate prior university training and specific technical and subject-related skills. You will need to provide documentation that shows the content and structure of your previous studies, proof of required knowledge areas (data science, machine learning, statistics/empirical research, and programming), and standard application documents such as a CV, language certificates and identity documentation. Some applicants (from India, China or Vietnam) must also submit an APS certificate. All documents should be complete and translated into the required language where applicable.
Prepare clear, chronological evidence of your academic record (including detailed transcripts/syllabi), a tabular CV covering your history from age 16, and formal proofs for each of the specialized competences listed below. If you have professional or non-university qualifications relevant to the field, include documentation for those as well. Answer any subject-specific test questions included in the application.
Admission requirements (bullet points)
Winter Semester (International)
1 June 2026
Winter Semester (EU/EEA)
1 June 2026
Graduates are prepared for roles at the interface of healthcare and technology: system architects, data engineers, machine learning specialists and product managers in health‑tech companies, hospitals and medical device firms, as well as positions in public health agencies and research institutions. The combination of technical depth, domain knowledge and soft skills also equips alumni to join startups or pursue entrepreneurial projects in digital health.
Because the programme stresses secure system design, regulatory and ethical understanding, and applied research experience, graduates are positioned to contribute to the development, evaluation and deployment of data‑driven clinical decision support, telemedicine platforms, digital diagnostics and other innovations that improve care quality and efficiency.
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