OBJECTIVES: Decision-making about health technologies from research and development to reimbursement is affected by heterogeneous opinion and criteria of participating stakeholders. There is a lack of systematic consideration of stakeholders’ preferences in current health policy. Multi-criteria decision analysis (MCDA) offers a solution to take preferences into account. This study gives an overview about the applications of MCDA methods in health economic decision-making. METHODS: A systematic review of the healthcare literature was performed to identify original research articles. Using predefined categories, data was systematically extracted for the type of policy applications, MCDA methodology, criteria used and how they were defined. RESULTS: 21 studies were included in the analysis. 12 studies (67%) used indirect approaches and nine studies (43%) used direct MCDA approaches. Four studies (19%) focused on technologies in the early innovation process. The majority of 17 studies (81%) examined a technology on the level of reimbursement decision. Eight studies (38%) resulted into implementation, which means that an official committee considered the MCDA results. Other studies were conducted in an explorative manner. MCDA decision criteria used were obtained from literature research and context-specific studies, expert opinions, and group discussions. The number of criteria ranged from three up to 15. The most prominent criteria described health outcomes (71%), disease impact (62%), and implementation of the intervention (38%). CONCLUSIONS: MCDA is used at different levels of medical innovation and can increase transparency for involved stakeholders by explicitly structuring decision criteria. Further research is needed to understand existing variability of the number of criteria used and ensure that models are robust regarding potential criteria overlaps, performance scales and operationalizability aligned with evidence needs. This project is supported by the German Federal Ministry of Education and Research (BMBF) as part of the National Cluster of Excellence ‚Medical Valley EMN’ (Project grant No. 13EX1013B).