Background: Grading of Recommendations Assessment, Development and Evaluation (GRADE) is now commonly used to grade the quality of outcomes based on systematic reviews such as Cochrane reviews and health technology assessments (HTAs). Model based health economic evaluations are an important part of HTA reports in several countries. Policy recommendations are frequently based on this type of analysis. A Bayesian framework is often applied to these economic evaluations. In a Bayesian framework, models for economic evaluation should be based on parameters with uncertainty described by a probability distribution. For efficacy parameters based on meta-analyses, it is common to use probability distributions based on the mean and standard error from a meta-analysis. This does however not reflect the uncertainty related to study quality. Objectives: To explore the usefulness of GRADE in health economic evaluations. Methods: We incorporated results from GRADE into all efficacy parameters indifferent models developed by the Norwegian Knowledge Centre for the Health Services (NorCaD, MOON, COSMO and MOCCA). Models were the run both with GRADE incorporated and without to show whether there was any difference in cost-effectiveness. Results: We performed regular cost-effectiveness analyses which showed substantial differences in results when GRADE was incorporated, compared to when GRADE was not incorporated. We also performed some expected value of perfect information analyses which showed that incorporating quality of trials in analyses would imply that it is more cost-effective to conduct new research. Conclusions: The use of GRADE in health economic evaluations gives results that are different and changes the uncertainty around the conclusions. If GRADE is an appropriate tool to assess the quality of outcomes, then we believe that the inclusion of GRADE in health economic evaluations is more appropriate than not including it.