Integrating easy-to-extract structured information such as medication and treatments into current natural language processing based systems can significantly boost coding performance; in this paper, we present a system that rigorously attempts to validate this intuitive idea. Based on recent i2b2 challenge winners, we derive a strong language model baseline that extracts patient outcomes from discharge summaries. Upon incorporating additional clinical cues into this language model, we see a significant boost in performance to F1 of 88.3 and a corresponding reduction in error of 23.52%.