Medical Test Order Parsing With Context Verification
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Solution Overview
Problem
Current methods for translating medical test orders into industry standard codes are error-prone, resource-intensive, and delay sample processing due to manual handling, especially when orders are provided in non-standard formats like handwritten notes or speech.
Innovation Solution
A computer-implemented method that parses and verifies medical test orders using context data to ensure accurate translation into industry standard test specifications, utilizing character and speech recognition, and machine learning to validate the candidate test order.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual translation of medical test orders is used, then translation accuracy can be maintained, but resource consumption increases and processing time delays occur
Solution Approach 1:
The patent replaces manual mechanical translation processes with an automated computer vision system. The system captures images of handwritten test orders, uses optical character recognition (OCR) to extract text, and automatically translates them into standardized digital test orders, eliminating the need for manual transcription while maintaining accuracy through verification mechanisms.
Solution Approach 2:
The system enables self-service translation by automatically processing test orders without requiring manual intervention. The automated pipeline handles image capture, text extraction, translation, and verification independently, allowing the system to serve itself rather than requiring human operators to manually translate each order.
2Productivity
If existing speech-to-text or text recognition systems are used, then translation speed increases, but error rates increase due to lack of context verification
Solution Approach 1:
The patent implements a feedback mechanism where the translated test order is verified against the original handwritten image and contextual information. The system checks for consistency between the extracted text and the visual appearance, and validates against known test protocols, allowing corrections to be made automatically based on feedback from the verification step.
Solution Approach 2:
The system performs preliminary actions by capturing the original handwritten test order image and preserving it as reference context before translation occurs. This preliminary capture of the source material allows the verification step to compare the translated output against the original, enabling error detection and correction before the translation is finalised.
3Reliability
If manual translation is performed, then translation accuracy can be maintained, but processing time increases
Solution Approach 1:
The patent replaces manual translation operations with automated computer vision and optical character recognition systems that can process test orders instantly. The electronic system captures, extracts, and translates text much faster than human operators, dramatically reducing processing time while maintaining accuracy through automated verification.
4Productivity
If automated translation without verification is used, then processing speed increases, but error rates increase
Solution Approach 1:
The system incorporates a verification step that provides feedback on the accuracy of translated test orders. The translated output is compared against the original handwritten image and validated against known test protocols, allowing the system to detect and correct errors automatically, thus maintaining high accuracy while preserving fast processing speeds.
Data Source
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AI summary
A computer-implemented method of processing a sample based on a digitized medical test order is provided. The computer-implemented method comprising the following steps: receiving, at a data processing agent, the digitized medical test order, the digitized medical test order comprising an image or a sound file comprising instructions for processing the sample; estimating, by the data processing agent, candidate text from the digitized medical test order by performing character recognition on the image or speech recognition on the sound file; comparing, by the data processing agent, the estimated candidate text to a set of predetermined test specifications to propose a candidate test order being requested by the digitized medical test order; and verifying, by the data processing agent, the candidate test order utilising context data associated with the digitized medical test order. When the candidate test order is successfully verified, the method comprises transmitting, by the data processing agent, the verified candidate test order to a control unit of an automated laboratory. The control unit is configured to match the sample to the verified candidate test order and instruct an automated analyzer of the automated laboratory to process said sample according to the verified candidate test order to obtain a test result.