The need for and challenges of educating and informing patients are well known and these are even greater for patients with low levels of literacy. Furthermore, as the population ages and with the increase in prevalence of chronic diseases where patient self-management is essential to holding disease in abeyance, patient education becomes increasingly important. With the advent of electronic medical records, there is an opportunity for automated tools to assist in addressing these challenges. In this paper we report on one approach to recommending relevant educational articles to patients. We attempt to infer the patient's information needs from his/her electronic medical records and use topic modeling to identify and match topics. A manual evaluation of the articles recommended by the proposed method showed that these articles are significantly more relevant (p < 0.01) to the patient's disease state than articles selected at random from within the same disease domain.