The course content lean on Design thinking ideology and methodology that inform research as well as developmental work and form a practice-based foundation for innovation aimed to meet the needs of societal and scientific environments. As a student, you work  individually from your own research perspective, and get helpful insights for future research.  

The course apply a problem-oriented teaching and learning style,  with a pedagogy that enables students to take active responsibility for individual and also group learning in a multi-disciplinary context.

Progress in medical innovation stands as a catalyst for worldwide economic growth. Whether within pharmaceuticals, medical devices, biotechnology, information technology, or a fusion of these advancements, the potential advantages extend significantly to private businesses and societal well-being. However, due to long development times and rigorous regulations, these innovative concepts require a lot of financing to establish their presence in the market. Consequently, assessing the value of novel medical innovations remains crucial. The primary objective of this course is to empower researchers with the proficient capability to effectively assess the worth of emerging technologies or processes within the medical industry.

This course aims to introduce students to health care organization and management, and how this affects public health. There is a special focus on the opportunities offered by digitization and how these can be utilized in quality and improvement work.

This course provides students with in-depth knowledge in the field of digital health from an entrepreneurship perspective. Domains of digital health, needs-based innovation including prototyping, usability and testing as well as data management, intellectual property, reimbursement, business models, ethics and future trends will be discussed and analyzed.

The Statistics module covers:

Basic test theory; 2-tests for contingency tables; t-Test; non-parametric tests; analysis of variance (ANOVA); multiple testing; power calculations; calculation rules for probabilities and neurobiological applications; guidelines for choice of analysis strategy; software implementations; effect size based hypothesis testing

The Scientific writing module:

• Introduction into general guidelines and rules for scientific writing
• Introduction into the elements of style
• Analysis and discussion of scientific texts
• How to improve and correct a text
• Practices in writing. Students will write their own texts and correct and make suggestions for improvements of the texts of others

Research ethics module covers:

• Main approaches and methods in current research ethics
• Ethical standards of good scientific practice
• Ethical issues related to research with humans
• Ethical issues related to animals
• Ethical issues related to research with biological material