The Electronic Health Record (EHR) comes laden with an ambitious array of promises: at the point of patient care, it will improve quality of documentation, reduce cost of care, and promote patient safety; in parallel, as more and more data are collected about patients, the EHR gives rise to exciting opportunities for mining patient characteristics and holds out the hope of compiling comprehensive phenotypic information. Leveraging the information present in the EHR is not a trivial step, however, especially when it comes to the information conveyed in clinical notes. In this talk I will focus on one of the challenges faced by the EHR and its users: information overload. With ever-growing longitudinal health records, it is difficult for clinicians to keep track of what is salient to their information needs when treating individual patients. I will present two applications and their methods -- both related to the goal of reducing complexity of the observations present in the patient record: HARVEST, a real-time summarization system deployed at NewYork-Presbyterian Hospital, and the Phenome model, which learns interpretable representations of diseases, or phenotypes, from large cohorts of patients.