AI Stroke Triage Routing for Faster Specialist Notification
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Solution Overview
Problem
Current triaging workflows in emergency settings are time-consuming, particularly for conditions like stroke, where every minute counts, leading to a decrease in treatment options and potential neuronal loss.
Innovation Solution
A computer-aided triage system that includes a router, remote computing system, and client application to rapidly analyze imaging data from a first point of care, detect suspected conditions, and notify specialists, reducing the time to transfer patients to specialists.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a standard radiology workflow is used for stroke assessment, then diagnostic accuracy is maintained, but the time to treat and transfer patient increases significantly
Solution Approach 1:
The system performs preliminary automated detection and classification of stroke patients using machine learning models before the standard radiology workflow begins. The deep learning model analyzes imaging data immediately upon arrival, pre-identifies potential stroke cases, and triggers automated notification workflows, so that by the time the radiologist reviews the images, the patient has already been flagged and prepared for rapid transfer.
Solution Approach 2:
The patent introduces an intermediary automated notification system that acts as a mediator between the imaging modality and the specialist. When the deep learning model detects a suspected stroke, it automatically sends notifications through a communication system to the designated specialist, eliminating the need for manual review and transmission steps in the traditional workflow.
2Loss of time
If automated detection systems are implemented, then time to notify specialist is reduced, but system complexity increases
Solution Approach 1:
The router serves multiple functions: it receives imaging data from various modalities, transmits data to the deep learning model, processes results, and sends notifications to specialists. This multi-functional design consolidates what would otherwise require separate systems into a single integrated unit, reducing overall system complexity while maintaining rapid detection capabilities.
Solution Approach 2:
The automated detection system performs self-service by automatically analyzing imaging data, making diagnostic suggestions, and initiating notification workflows without requiring continuous human intervention. The deep learning model continuously processes incoming images and triggers appropriate responses autonomously, reducing the burden on staff while maintaining high-speed detection.
3Speed
If deep learning models are used for condition detection, then detection speed increases, but false positives may occur
Solution Approach 1:
The system incorporates feedback mechanisms where the deep learning model's detection suggestions are reviewed and validated by radiologists or clinical staff. The model continuously learns from these feedback loops, adjusting its detection criteria to reduce false positives while maintaining high detection speed. Clinical staff can correct misclassifications, and the model updates its parameters accordingly.
Solution Approach 2:
The system uses partial action by providing detection suggestions rather than definitive diagnoses, allowing human professionals to make the final determination. This approach enables the AI to operate at full speed for initial screening while the human element provides the necessary reliability check, accepting that some false positives will occur but can be easily corrected by staff review.
Data Source
AI summary
A system for computer-aided triage includes a router, a remote computing system, and a client application. Additionally or alternatively, the system 100 can include any number of computing systems, servers, storage, lookup table, memory, and/or any other suitable components. A method for computer-aided triage includes receiving a data packet associated with a patient and taken at a first point of care; checking for a suspected condition associated with the data packet; in an event that the suspected condition is detected, determining a recipient based on the suspected condition; and transmitting information to a device associated with the recipient.