Natural Language Processing (NLP) is a powerful technique for extracting valuable information from unstructured electronic health records (EHRs). However, a prerequisite for NLP is the availability of high-quality annotated datasets. To date, there is a lack of effective methods to guide the research effort of manually annotating unstructured datasets, which can hinder NLP performance. Therefore, this study develops a five-step workflow for manually annotating unstructured datasets, including (1) annotator training and familiarising with the text corpus, (2) vocabulary identification, (3) anno...