Objective: Point of care access to knowledge from full text journal articles supports decision making and decreases medical errors. However, it is an overwhelming task to obtain full text for all journals and the quality of information for a specific topic such as Congestive Heart Failure (CHF) varies across the journals. In this paper, we develop a method to automatically rate journals for a given clinical topic to enable filtering journals or ranking the articles based on source journal. Materials and Methods: We surveyed 169 cardiologists chosen across the US that practice medicine and publish on the topic of CHF. They provided subjective opinion on how valuable the information provided in 60 journals (chosen by Mayo Clinic cardiologists) is for their clinical decision-making. Results: We identified the top CHF journals chosen by cardiologists and the objective metrics that correlate with their scores. Our best Multiple Linear Regression model has a correlation of 0.880 based on five-fold cross-validation. Discussion: We demonstrated that general journal metrics such as impact factor, h-index and number of articles per year provide better results when used in combination with topic-specific metrics such as number of abstracts indexed with the corresponding MeSH® terms. Conclusion: We obtained a journal priority score formula based on different journal metrics to automatically rate any journal in relation to its perceived importance in CHF. Our study shows that using this formula might yield a reliable list of prioritized journals even across other clinical topics such as Multiple Sclerosis.