The feasibility study is a collaborative project together with KTH and Stockholm Science City (SSCI) and aims to investigate the possibilities of using synthesis as a method to make health data available to companies, researchers, health care providers and other actors in a safe way that does not conflict with the General Data Protection Regulation, GDPR, to contribute to safer and more equal care. To be able to develop methods within artificial intelligence (AI) and machine learning (ML) that can benefit healthcare requires large amounts of relevant and validated data. The primary target group is small and medium-sized companies (SMEs) that develop AI and ML algorithms as well as those that develop processes, tools and treatments for healthcare. Goal The main objective of the feasibility study is to investigate whether synthesis is a useful method for safely sharing healthcare data without conflicting with current legislation and thereby creating value of the care data in the VAL database. The milestones: 1) Find a method that allows a synthetic dataset to achieve the same quality and thus usability as original data. This is done through a selection of parameters from the VAL database. 2) Find a method that has sufficiently high level of synthesis to ensure that the legal barriers to the disclosure of this data are affordable. This includes a technical evaluation where one can trace back to an individual and a legal evaluation that ensures that the data is not contrary to the GDPR. 3) Get a good understanding of the target group’s needs in terms of health data and also gain an understanding of the critical factors of the target group based on sustainability aspects. The feasibility study is expected to have generated a basis for a possible implementation project.