AI Transfusion Decision Support System
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Solution Overview
Problem
Current blood transfusion management systems lack the ability to account for individual patient variables, leading to inappropriate transfusions and increased risks of adverse side effects, and they lack transparency and traceability, resulting in higher mortality, morbidity, and healthcare complications.
Innovation Solution
A computer-implemented system using artificial intelligence (AI) and machine learning (ML) technologies to determine the appropriateness of blood transfusions by analyzing patient-specific data, historical transfusion data, and domain knowledge, providing a transfusion appropriateness score and recommending the type, amount, and timing of blood products, while also tracking and reporting transfusion procedures for improved decision-making and resource management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If physician discretion is used to determine transfusion candidates, then ease of operation is improved, but manufacturing precision deteriorates
Solution Approach 1:
The patent replaces the mechanical system of human physician discretion with an automated AI-based decision support system that processes patient data, medical history, and clinical parameters to generate transfusion recommendations, thereby substituting human judgment with algorithmic precision
Solution Approach 2:
The system changes the parameters of transfusion decision-making by incorporating multiple quantifiable factors including patient-specific variables, laboratory values, comorbidities, and risk stratification criteria into a structured analytical framework that produces objective appropriateness scores
2Reliability
If current blood management systems are used, then device complexity is reduced, but reliability deteriorates
Solution Approach 1:
The patent creates a multi-functional system that simultaneously performs patient identification, risk assessment, appropriateness scoring, recommendation generation, and tracking functions within a single integrated platform, making the system universally applicable across different transfusion scenarios
Solution Approach 2:
The AI-based decision support system acts as an intermediary between available patient data and clinical transfusion decisions, processing and analyzing multiple data sources to generate evidence-based recommendations that bridge the gap between raw data and clinical action
3Object-affected harmful factors
If individual patient variables are not accounted for, then device complexity is reduced, but object-affected harmful factors increase
Solution Approach 1:
The patent applies local quality by tailoring the transfusion assessment to each individual patient's specific characteristics, medical history, comorbidities, and clinical parameters, ensuring that the analysis is customized to the unique needs and risks of each patient rather than applying uniform criteria
4Loss of information
If current blood management systems are used, then loss of substance is reduced, but loss of information increases
Solution Approach 1:
The system implements feedback mechanisms by providing detailed analytics and reporting at the patient and physician level, tracking transfusion procedures and outcomes, and generating transparency through documented decision-making processes that feed back into continuous improvement and accountability
Data Source
AI summary
Method and apparatus are described for a system that employs a change management algorithm to drive transfusion “appropriateness” by factoring evidenced-based knowledge and input from practitioners, where said algorithm may also ensure that a blood or blood product transfusion is provided to the right patient, that the blood/blood product is transfused at the right time, and that the procedure is completed for the right reason.