  The language used by physicians and health professionals in prescription
directions includes medical jargon and implicit directives and causes much
confusion among patients. Human intervention to simplify the language at the
pharmacies may introduce additional errors that can lead to potentially severe
health outcomes. We propose a novel machine translation-based approach,
PharmMT, to automatically and reliably simplify prescription directions into
patient-friendly language, thereby significantly reducing pharmacist workload.
We evaluate the proposed approach over a dataset consisting of over 530K
prescriptions obtained from a large mail-order pharmacy. The end-to-end system
achieves a BLEU score of 60.27 against the reference directions generated by
pharmacists, a 39.6% relative improvement over the rule-based normalization.
Pharmacists judged 94.3% of the simplified directions as usable as-is or with
minimal changes. This work demonstrates the feasibility of a machine
translation-based tool for simplifying prescription directions in real-life.
