Automated Extraction Of Clinical Tables And Forms From Scanned Medical Records Using Computer Vision And Layout-Aware Machine Learning

Abstract

Healthcare institutions process millions of unstructured clinical documents each year, including laboratory reports, discharge summaries, imaging results, and handwritten charts. Most of this information remains trapped in image or portable document format files, limiting its usefulness for population health management, predictive analytics, and quality reporting measures. This article outlines a layout-aware computer vision methodology that incorporates deep learning architectures and bounding-box reconstruction algorithms to convert clinical documents into structured data repositories. Building upon advances in table recognition, column segmentation, and optical character recognition post-processing, the proposed system handles both gridded and non-gridded tabular formats while addressing fragmented borders and discontinuous line elements. Clinical entities, including test nomenclature, quantitative results, and reference intervals, are mapped into standardized schemas that conform to Fast Healthcare Interoperability Resources (FHIR) specifications. Validation protocols, electronic health record integration pathways, and compliance with the Health Insurance Portability and Accountability Act (HIPAA) standards, alongside the Office of the National Coordinator's interoperability mandates, form essential components of the framework. Expected benefits encompass decreased manual abstraction costs, strengthened clinical decision support systems, and improved real-time risk assessment for chronic disease management.