  Access to real-world medication prescriptions is essential for medical
research and healthcare quality improvement. However, access to real medication
prescriptions is often limited due to the sensitive nature of the information
expressed. Additionally, manually labelling these instructions for training and
fine-tuning Natural Language Processing (NLP) models can be tedious and
expensive. We introduce a novel task-specific model architecture,
Label-To-Text-Transformer (\textbf{LT3}), tailored to generate synthetic
medication prescriptions based on provided labels, such as a vocabulary list of
medications and their attributes. LT3 is trained on a set of around 2K lines of
medication prescriptions extracted from the MIMIC-III database, allowing the
model to produce valuable synthetic medication prescriptions. We evaluate LT3's
performance by contrasting it with a state-of-the-art Pre-trained Language
Model (PLM), T5, analysing the quality and diversity of generated texts. We
deploy the generated synthetic data to train the SpacyNER model for the Named
Entity Recognition (NER) task over the n2c2-2018 dataset. The experiments show
that the model trained on synthetic data can achieve a 96-98\% F1 score at
Label Recognition on Drug, Frequency, Route, Strength, and Form. LT3 codes and
data will be shared at
\url{https://github.com/HECTA-UoM/Label-To-Text-Transformer}
