mlsanityData to 14 Oct 2026

PharmMT: A Neural Machine Translation Approach to Simplify Prescription Directions

arXiv 2204.03830 ↗First posted 8 Apr 2022cs.CL cs.AIcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 18 of its 49 references that are in the map. First posted on arXiv 8 Apr 2022.

Builds on

18 of 49 references.

References · 18

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6 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 0; 2026: 1.

2 papers.

  1. 2022Retrieval augmentation of large language models for lay language generationcited by 21
  2. 2026From Triage to Discharge: A Survey of NLP Tasks, Methods, and Open Challenges in the Emergency Departmentcited by 1