Fig. 1: High-level overview of the study and pharmacy workflow.
From: Large language models for preventing medication direction errors in online pharmacies
a, Schematic of the pharmacy workflow used, highlighting the occurrence of near-miss events, the primary metric of the prospective evaluation. b, Examples of pairs of prescriber medications directions and their corresponding, pharmacist verification (PV) equivalents. This process occurs in the DE technician step as highlighted in the previous panel. c, Different LLM-based strategies were used to generate pharmacist-approved medication directions from prescriber directions, highlighting their corresponding data requirements and training methodology. d, Types of evaluation and metric utilized to assess the performance of each AI approach. e, Data description and how the data were used in the study to train and evaluate the different AI approaches.
