Automated Digitization of Unstructured Medical Prescriptions

Megha Sharma, Tushar Vatsal, Srujana Merugu, Aruna Rajan

Abstract
Automated digitization of prescription images is a critical prerequisite to scale digital healthcare services such as online pharmacies. This is challenging in emerging markets since prescriptions are not digitized at source and patients lack the medical expertise to interpret prescriptions to place orders. In this paper, we present prescription digitization system for online medicine ordering built with minimal supervision. Our system uses a modular pipeline comprising a mix of ML and rule-based components for (a) image to text extraction, (b) segmentation into blocks and medication items, (c) medication attribute extraction, (d) matching against medicine catalog, and (e) shopping cart building. Our approach efficiently utilizes multiple signals like layout, medical ontologies, and semantic embeddings via LayoutLMv2 model to yield substantial improvement relative to strong baselines on medication attribute extraction. Our pipeline achieves +5.9% gain in precision@3 and +5.6% in recall@3 over catalog-based fuzzy matching baseline for shopping cart building for printed prescriptions.
Anthology ID:
2023.acl-industry.76
Volume:
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Sunayana Sitaram, Beata Beigman Klebanov, Jason D Williams
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
794–805
Language:
URL:
https://aclanthology.org/2023.acl-industry.76
DOI:
10.18653/v1/2023.acl-industry.76
Bibkey:
Cite (ACL):
Megha Sharma, Tushar Vatsal, Srujana Merugu, and Aruna Rajan. 2023. Automated Digitization of Unstructured Medical Prescriptions. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track), pages 794–805, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Automated Digitization of Unstructured Medical Prescriptions (Sharma et al., ACL 2023)
Copy Citation:
PDF:
https://preview.aclanthology.org/dois-2013-emnlp/2023.acl-industry.76.pdf
PDF Search