Radiology Protocol Recommendation Using ML Order Classification
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Solution Overview
Problem
Conventional methods for determining radiology protocols are time-consuming, expensive, and inconsistent due to manual review of imaging examination orders and patient medical records, which include structured and unstructured data in varying formats, and site-specific variability in radiology protocol expression.
Innovation Solution
A method involving a machine learning model to convert unstructured text into feature vectors, map structured data and vectors to a standardized radiology protocol representation, and translate this to a site-specific protocol using a site-specific translator, enabling efficient and consistent protocol recommendation across different healthcare sites.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual review of imaging examination orders and patient medical records is used to determine radiology protocols, then protocol accuracy and customization can be maintained, but time consumption and operational costs increase significantly
Solution Approach 1:
The patent replaces the manual mechanical review process with an automated machine learning system. The ML model processes imaging examination orders and patient medical records automatically, eliminating the need for radiologists or technologists to manually review each case, thus reducing time consumption while maintaining protocol accuracy through learned patterns from training data.
Solution Approach 2:
The system enables self-service automation where the machine learning model independently performs protocol selection without human intervention. The model autonomously processes unstructured text, converts it to feature vectors, and selects appropriate radiology protocols based on learned patterns, allowing the system to serve itself rather than requiring continuous human oversight.
2Reliability
If manual review by radiologists or technologists is performed, then protocol customization to patient具体情况 can be achieved, but operational costs increase
Solution Approach 1:
The patent substitutes the expensive manual labor of radiologists and technologists with an automated machine learning system. The ML model processes patient-specific information and selects customized protocols without human intervention, significantly reducing operational costs while maintaining the ability to personalize protocols to individual patient needs through learned patterns from training data.
3Productivity
If automation of radiology protocol selection is implemented, then speed and consistency of imaging workflow can be improved, but the system becomes vulnerable to format variability in imaging examination orders and medical records
Solution Approach 1:
The patent applies parameter changes by converting unstructured text from various formats into standardized feature vectors. The machine learning model processes imaging examination orders and medical records in different formats, transforms them into a unified representation, and outputs consistent protocol recommendations, thereby achieving both speed and robustness to format variability.
Solution Approach 2:
The patent introduces an intermediary layer in the form of feature vectors that mediates between the variable input formats and the standardized radiology protocol outputs. The machine learning model first converts diverse unstructured text formats into feature vectors, then maps these to consistent protocol recommendations, effectively handling format variability while maintaining workflow speed and consistency.
4Adaptability or versatility
If site-specific radiology protocols are used to accommodate local variations, then local customization is achieved, but system scalability across multiple sites becomes difficult
Solution Approach 1:
The patent implements universality by training a single machine learning model on diverse data from multiple sites with different protocol formats. The model learns to handle various site-specific variations and can generalize to new sites without requiring retraining or site-specific customization, thus achieving both local customization and system scalability across multiple healthcare facilities.
Data Source
AI summary
Various methods and systems are provided for automatically recommending one or more radiology protocols based on an imaging examination order which includes both structured and unstructured data. In one example, a method includes receiving an imaging examination order requesting an imaging examination, wherein the imaging examination order comprises structured data and unstructured text, converting the unstructured text into one or more feature vectors, mapping the structured data and the one or more feature vectors to a standardized radiology protocol representation using an imaging examination order classifier, and mapping the standardized radiology protocol representation to a site-specific radiology protocol using a site-specific radiology protocol translator.