Document Understanding for Healthcare Referrals
Jimit Mistry Natalia M. Arzeno
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.