Document Understanding for Healthcare Referrals
This addresses administrative inefficiencies and errors in healthcare referrals, though it appears incremental as it builds on existing models with domain-specific adaptations.
The researchers tackled the problem of extracting key entities from faxed healthcare referral documents by proposing a hybrid model combining LayoutLMv3 with domain-specific rules, which achieved greatly increased precision and F1 scores.
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.