RxLens: Multi-Agent LLM-powered Scan and Order for Pharmacy
Abstract
AbstractAutomated construction of shopping cart frommedical prescriptions is a vital prerequisite forscaling up online pharmaceutical servicesin emerging markets due to the high prevalence of paper prescriptionsthat are challenging for customers to interpret.We present RxLens, a multi-step end-end Large Language Model (LLM)-based deployed solutionfor automated pharmacy cart construction comprisingmultiple steps: redaction of Personal Identifiable Information (PII),Optical Character Recognition (OCR), medication extraction, matching against the catalog, and bounding box detection for lineage. Our multi-step design leverages the synergy between retrieval and LLM-based generationto mitigate the vocabulary gaps in LLMs and fuzzy matching errors during retrieval.Empirical evaluation demonstrates that RxLens can yield up to 19% - 40% and 11% - 26% increase in Recall@3 relative to SOTA methods such as Medical Comprehend and vanilla retrieval augmentation of LLMs on handwritten and printed prescriptions respectively.We also explore LLM-based auto-evaluation as an alternative to costly manual annotations and observe a 76% - 100% match relative to human judgements on various tasks.