Scanned Document OCR and NLP for Structured Medical Data Extraction
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Solution Overview
Problem
Scanned documents, particularly medical records, are stored electronically but remain largely unusable without manual curation due to challenges in processing structured data such as tables, hindering integration into electronic health records (EHRs) and preventing effective use by advanced analytical methods.
Innovation Solution
A method and system that employs optical character recognition (OCR) and natural language processing (NLP) to detect and extract structured data from scanned documents, including cell and table boundaries, and convert text and data into usable formats for integration into EHR systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If scanned documents are stored as electronic images, then storage and transmission are enabled, but the data becomes unusable for clinical care and research without manual review
Solution Approach 1:
The patent introduces an intermediary processing system that includes OCR engines and NLP algorithms. These intermediaries convert scanned document images into structured, machine-readable data that can be integrated into EHR systems, thereby resolving the contradiction between automated storage and data usability.
Solution Approach 2:
The patent replaces the manual review process (mechanical human labor) with automated computational systems including OCR and NLP algorithms. This substitution enables large-scale processing of scanned documents while maintaining or improving data usability for clinical and research purposes.
2Productivity
If manual review is used to extract data from scanned documents, then data accuracy is maintained, but processing time and resource requirements increase significantly
Solution Approach 1:
The patent implements feedback mechanisms where the automated system's output is validated and refined. The NLP algorithms learn from the structured data extracted by OCR and continue to improve accuracy over time, while feedback loops allow for correction and verification of extracted information, thereby maintaining precision while improving productivity.
Solution Approach 2:
The patent performs preliminary OCR processing to convert images to text before NLP analysis. This preliminary action prepares the data in advance, making subsequent extraction and validation more efficient and accurate, thus improving both processing speed and data quality.
3Adaptability or versatility
If regional information exchanges are used to share scanned documents, then interoperability between systems is achieved, but the data remains inaccessible for operational and research needs
Solution Approach 1:
The patent creates a universal data extraction system that can process various types of scanned documents (medical records, lab results, etc.) from different sources. The standardized output format enables the system to serve multiple functions including clinical care, administrative operations, and research, thereby improving adaptability without proportionally increasing complexity.
Data Source
AI summary
Automated systems and methods for retrieving information from scanned documents are described herein. An example method includes reading a scanned document; and extracting a page image from the scanned document, where the page image includes text and structured data. The method also includes executing a structured data detection algorithm to detect the structured data contained in the page image and identify a plurality of elements of the structured data. Additionally, the method includes performing optical character recognition (OCR) to convert the text contained in the page image to first text data; and performing OCR to convert respective contents of each of the plurality of elements of the structured data to second text data. The method further includes executing a natural language processing (NLP) algorithm to retrieve information from the first and second text data.