AI Clinical Decision Support System for Treatment Optimization
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Solution Overview
Problem
Current healthcare systems face challenges in making optimal treatment decisions due to complex and rapidly expanding information streams, high costs, and a gap between research and practice, with less than 50% of patients receiving correct diagnoses and treatments, and a 13-17 year lag in implementing research findings into clinical care, leading to inefficient and costly healthcare.
Innovation Solution
A computational artificial intelligence (AI) framework that provides a simulation environment for predicting treatment outcomes, combining autonomous AI with human clinicians to enhance decision-making, using patient monitoring data, electronic health records, and genetic information to create personalized treatment plans through a multi-agent system that evaluates decision-outcome nodes with a cost per unit change function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional clinical decision-making methods are used, then human clinicians can make treatment decisions, but the accuracy and optimality of treatment choices deteriorate due to information overload and complexity
Solution Approach 1:
The patent introduces an AI-based clinical decision support system as an intermediary between the complex healthcare information ecosystem and human clinicians. The system processes electronic health records, genomic data, clinical guidelines, and research literature through natural language processing and knowledge graph technologies to generate synthesized clinical insights, thereby reducing the information burden on clinicians while improving diagnostic accuracy and treatment optimization.
2Adaptability or versatility
If more comprehensive patient data and treatment options are collected, then treatment personalization improves, but the time and computational resources required increase
Solution Approach 1:
The system performs preliminary actions by continuously pre-processing and structuring healthcare data through knowledge graphs and natural language processing pipelines. Clinical guidelines, research literature, and patient records are pre-synthesized into structured knowledge representations before clinical decisions are needed. This allows the system to rapidly retrieve and present relevant personalized treatment recommendations without time-consuming data processing during critical decision moments.
Solution Approach 2:
The patent replaces manual mechanical processes of data collection, synthesis, and analysis with automated AI systems. Machine learning models automatically process electronic health records, genomic data, and clinical literature, substituting the time-intensive manual work of clinicians in reviewing and synthesizing information. This automation maintains high personalization while dramatically reducing decision-making time.
3Reliability
If autonomous AI systems are deployed for clinical decision support, then decision accuracy improves, but the system complexity and implementation costs increase
Solution Approach 1:
The system implements multi-layered feedback mechanisms including confidence scoring that indicates the reliability of AI-generated recommendations, explainability features that show the reasoning behind decisions, and continuous learning from clinical outcomes. This feedback loop allows the system to improve decision reliability over time while providing transparency to clinicians, thereby managing system complexity through iterative improvement rather than requiring perfect initial complexity.
4Measurement precision
If extensive clinical training is provided to human doctors, then decision-making quality improves, but the training time and costs increase
Solution Approach 1:
The system creates a digital copy of expert clinical reasoning capabilities through AI models trained on extensive medical literature, guidelines, and expert decision-making patterns. Instead of requiring each clinician to individually acquire this knowledge through lengthy training, the system encodes expert knowledge into algorithms that can be rapidly deployed and updated. This copying approach transfers expert-level decision quality to the system without requiring equivalent training time for individual practitioners.
Data Source
AI summary
The present invention involves a system and method of providing decision support for assisting medical treatment decision-making. A patient agent software module processes information about a particular patient. A doctor agent software module processes information about a health status of a particular patient, beliefs relating to patient treatments, and the actual effects of treatment decisions. By filtering information over time from the patient agent into the doctor agent, a plurality of decision-outcome nodes are created and formed into a patient-specific outcome tree with the plurality of decision-outcome nodes. An optimal treatment is determined by evaluating the plurality of decision-outcome nodes with a cost per unit change function to output the optimal treatment. When additional information is available from at least one of the patient agent and the doctor agent, the filtering, creating, and determining steps are repeated thus allowing for the system to “reason over time”, continuously updating and learning as new information is received.