OCR Isn't Good Enough: From Medical Faxes to Structured Data (robert-mcdermott.medium.com)

A recent proof of concept (PoC) project aimed at automating the intake of medical results fax processing highlighted significant shortcomings of traditional Optical Character Recognition (OCR) technologies. Many healthcare organizations still rely on antiquated systems, with lab results often arriving via fax. Initial attempts to extract data using OCR were fraught with inaccuracies, prompting a pivot to a vision-capable Large Language Model (LLM) that converts documents to Markdown format. This change led to a dramatic improvement in extraction accuracy, enabling better preservation of layouts and tables crucial for medical documentation. This innovation is particularly significant for the AI/ML community as it demonstrates the limitations of established OCR methods when dealing with low-quality scanned documents. The vision-LLM approach not only offers a more reliable solution but also highlights the necessity for modernizing healthcare data processing systems. By open-sourcing the created utility, Doc2MD, the project contributes to a growing suite of AI-driven tools aimed at transforming how medical documents are processed, ensuring greater efficiency and accuracy in medical record management.