Multi-Agent Patient Management Workflow System
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Solution Overview
Problem
Healthcare providers face challenges in stratifying patients and selecting optimal therapies based on imaging and medical data, lacking tools that integrate heterogeneous data and select the best tools for each step in the clinical workflow effectively.
Innovation Solution
A multi-agent system employing intelligent agents that use machine learning to analyze data and propose actions at each step of the patient management workflow, including identifying diagnostic tests, selecting processing techniques, and generating optimal patient management plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional technology is used for patient management, then the design of the optimal patient management plan is left to the clinician, but no tool is available that supports the integration of heterogeneous data and selection of the best available tool for each step in the clinical workflow
Solution Approach 1:
The system segments the complex patient management workflow into discrete decision points, with each intelligent agent specialized for a specific decision point. This segmentation allows the system to handle heterogeneous data types appropriately at each stage while maintaining overall system manageability through modular architecture.
Solution Approach 2:
Intelligent agents serve as intermediaries between heterogeneous data sources and clinical decision-making. Each agent integrates and processes relevant data from multiple sources (imaging, electronic health records, lab results) and translates this integrated information into actionable recommendations, bridging the gap between diverse data types and clinical workflows.
2Reliability
If multiple intelligent agents are employed to perform patient management workflow, then systematic and objective patient management is enabled, but workflow complexity increases
Solution Approach 1:
Each intelligent agent is designed with multi-functionality, capable of performing data integration, analysis, and recommendation generation within its specific decision point context. This universal design pattern across agents standardizes operations and reduces overall system complexity despite the presence of multiple agents.
Solution Approach 2:
The system implements feedback loops where intelligent agents continuously monitor workflow progress and adjust their recommendations based on outcomes from previous decisions. This feedback mechanism enhances reliability by enabling objective, data-driven adjustments while the automated nature of feedback processing actually reduces manual complexity.
3Productivity
If intelligent agents use machine learning to integrate and analyze available data, then the best available tool can be selected for each step, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models during system setup and pre-processing data as it becomes available. This allows the intelligent agents to make rapid decisions during actual patient management workflows without performing heavy computational tasks in real-time, thereby improving workflow efficiency while managing computational resource consumption.
Data Source
AI summary
A computer-implemented method for executing patient management workflows includes acquiring a pre-test dataset of clinically relevant information related to a patient and using a first intelligent agent to identify a diagnostic test for the patient based on the pre-test dataset. Following performance of the diagnostic test, a second intelligent agent is used to select a processing technique to be applied to data collected from the diagnostic test to obtain a diagnostic marker. Following application of the processing technique to the data collected from the diagnostic test, a third intelligent agent is used to generate an optimal patient management plan based on the pre-test dataset, the data collected from the diagnostic test, and the diagnostic marker.