Document Understanding for Healthcare Referrals
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866913234753159168 |
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| author | Mistry, Jimit Arzeno, Natalia M. |
| author_facet | Mistry, Jimit Arzeno, Natalia M. |
| contents | Reliance on scanned documents and fax communication for healthcare referrals leads to high administrative costs and errors that may affect patient care. In this work we propose a hybrid model leveraging LayoutLMv3 along with domain-specific rules to identify key patient, physician, and exam-related entities in faxed referral documents. We explore some of the challenges in applying a document understanding model to referrals, which have formats varying by medical practice, and evaluate model performance using MUC-5 metrics to obtain appropriate metrics for the practical use case. Our analysis shows the addition of domain-specific rules to the transformer model yields greatly increased precision and F1 scores, suggesting a hybrid model trained on a curated dataset can increase efficiency in referral management. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_13184 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Document Understanding for Healthcare Referrals Mistry, Jimit Arzeno, Natalia M. Computation and Language Information Retrieval I.2.7; I.7.5 Reliance on scanned documents and fax communication for healthcare referrals leads to high administrative costs and errors that may affect patient care. In this work we propose a hybrid model leveraging LayoutLMv3 along with domain-specific rules to identify key patient, physician, and exam-related entities in faxed referral documents. We explore some of the challenges in applying a document understanding model to referrals, which have formats varying by medical practice, and evaluate model performance using MUC-5 metrics to obtain appropriate metrics for the practical use case. Our analysis shows the addition of domain-specific rules to the transformer model yields greatly increased precision and F1 scores, suggesting a hybrid model trained on a curated dataset can increase efficiency in referral management. |
| title | Document Understanding for Healthcare Referrals |
| topic | Computation and Language Information Retrieval I.2.7; I.7.5 |
| url | https://arxiv.org/abs/2309.13184 |