Automated Pathology Report Extraction Using OCR and NLP
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Clinical data in pathology reports, often in paper or scanned image form, is difficult to access and analyze due to its unstructured nature, leading to laborious, time-consuming, and error-prone manual extraction processes for clinicians.
Innovation Solution
An automated workflow that uses optical character recognition (OCR) and natural language processing (NLP) to extract and enrich pathology entities from images of pathology reports, converting them into structured medical data aligned with standard terminologies like SNOMED.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual extraction of clinical data from pathology reports is performed, then data can be accessed and analyzed, but the process becomes laborious, time-consuming, and error-prone
Solution Approach 1:
The patent replaces the manual mechanical process of reading and extracting data from pathology reports with an automated optical character recognition (OCR) system. The OCR technology converts scanned images of pathology reports into machine-readable text, eliminating the need for clinicians to manually read through reports and extract clinical data, thereby significantly improving productivity while reducing time loss.
2Reliability
If clinical data is stored in paper or scanned image form, then data can be preserved, but accessibility and analysis become difficult
Solution Approach 1:
The patent creates a digital copy of the scanned pathology report images through OCR technology. The system generates both machine-readable text from the scanned images and structured data representations that preserve the original clinical information. This copying process maintains data reliability while dramatically improving accessibility, allowing clinicians to search, retrieve, and analyze clinical data efficiently without needing to physically handle or manually read paper or image-based reports.
3Loss of information
If unstructured clinical data is processed manually, then information can be extracted, but the process becomes costly and error-prone
Solution Approach 1:
The patent replaces complex manual processing operations with automated OCR and natural language processing systems. The technology automatically extracts structured clinical data including patient demographics, diagnosis, treatment history, and laboratory results from unstructured pathology reports. This substitution reduces human error while maintaining complete information extraction, and although the system complexity increases, it eliminates the need for manual labor and reduces operational costs.
4Measurement precision
If large scale processing of pathology reports is performed manually, then detailed insights can be obtained, but expense and time limitations make it infeasible
Solution Approach 1:
The patent replaces manual analysis operations with automated OCR and data processing systems that can handle large volumes of pathology reports simultaneously. The system extracts structured clinical data at scale, enabling detailed insights into healthcare delivery and quality of care across large patient populations. This automation maintains measurement precision by systematically extracting data according to predefined schemas while dramatically increasing productivity to overcome expense and time limitations.
Data Source
AI summary
In one example, a method being performed by a computer system comprises: receiving an image file containing a pathology report; performing an image recognition operation on the image file to extract input text strings; detecting, using a natural language processing (NLP) model, entities from the input text strings, each entity including a label and a value; extracting, using the NLP model, the values of the entities from the input text strings; converting, based on a mapping table that maps entities and values to pre-determined terminologies, the values of at least some of the entities to the corresponding pre-determined terminologies; and generating a post-processed pathology report including the entities detected from the input text strings and the corresponding pre-determined terminologies.