Automated Stroke Decision Support Tool for Transfer Optimization
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Solution Overview
Problem
Community hospitals lack the expertise and resources to make timely decisions regarding the transfer of stroke patients to tertiary hospitals for endovascular therapy, often resulting in unnecessary transfers and delayed treatment due to the complexity of endovascular procedures and the limited availability of these services.
Innovation Solution
An automated decision support tool that processes clinical and imaging data to determine whether a stroke patient should be transferred to a tertiary hospital for endovascular therapy or treated with thrombolytic drugs at the community hospital, considering factors like thrombus morphology, collateral blood flow, and treatment timelines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If endovascular therapy is provided at tertiary hospitals, then treatment efficacy is improved, but device complexity and resource requirements increase
Solution Approach 1:
The healthcare system is segmented into community hospitals for initial assessment and thrombolytic therapy, and tertiary hospitals for endovascular therapy. This segmentation allows complex procedures to be concentrated at specialized centers while keeping community hospitals focused on less complex interventions, resolving the contradiction between treatment efficacy and procedure complexity distribution
Solution Approach 2:
An automated decision support tool acts as an intermediary between community hospitals and tertiary hospitals. It processes clinical and imaging data to determine whether patients should be transferred for endovascular therapy, thereby mediating the complex decision-making process and reducing the burden on both community hospital physicians and tertiary hospital resources
2Reliability
If patients are transferred to tertiary hospitals for endovascular therapy, then treatment outcomes improve, but loss of time increases due to transfer logistics
Solution Approach 1:
The automated decision support tool performs preliminary assessment of patient eligibility for endovascular therapy at the community hospital level, using pre-collected clinical and imaging data. This preliminary action allows for rapid decision-making about transfer necessity, reducing the time lost in transfer logistics by avoiding unnecessary transfers and enabling immediate transfer decisions when appropriate
Solution Approach 2:
The manual decision-making process involving multiple physicians and consultations is replaced with an automated computational system that processes data and generates transfer recommendations instantly. This substitution eliminates the time delays associated with human deliberation and coordination, enabling rapid transfer decisions when endovascular therapy is indicated
3Measurement precision
If community hospitals perform thorough assessments for transfer decisions, then decision accuracy improves, but loss of time increases due to assessment complexity
Solution Approach 1:
The automated decision support tool performs the assessment function automatically by processing existing clinical and imaging data without requiring manual review by multiple physicians. The system serves itself by utilizing already-collected data from the imaging system and electronic health records, eliminating the need for time-consuming manual assessments while maintaining high decision accuracy through algorithmic analysis
Data Source
AI summary
An automated system and method for assisting in decision making for the treatment of stroke patients is provided, and specifically for assisting a physician whether the patient should be administered a drug or transferred to another hospital to undergo an endovascular thrombectomy procedure. A variety of factors are input into the system with limited human intervention and a tool automatically determines the probability of whether the patient will have a better outcome if transferred or not. The factors include clinical factors, imaging factors and time to transfer factors. The tool includes processes for automatically determining several imaging factors, including the determination of clot length, collateral blood flow, the presence of forward blood flow within and around the clot, and the clot permeability. The tool has capability to continuously update the treatment protocol and other output results using current clinical, health system or other relevant information or feedback.