Agent Orchestrator for Electrophysiology Procedure Guidance
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Solution Overview
Problem
Traditional electrophysiology procedures rely heavily on manual processes and physician expertise, leading to variability in outcomes.
Innovation Solution
A system and method utilizing an agent orchestrator to process user inputs, including medical sensing device data and electronic health records, to select and execute appropriate agents for generating responsive outputs, with a fallback protocol for reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual processes and physician expertise are used for electrophysiology procedures, then flexibility and adaptability are maintained, but variability in outcomes increases and procedural accuracy decreases
Solution Approach 1:
The system segments the complex electrophysiology procedure into multiple distinct agent functions: data acquisition agents, image processing agents, analysis agents, and guidance agents. Each agent handles a specific aspect of the procedure, ensuring standardized and precise execution while reducing variability through specialized automation.
Solution Approach 2:
The agent orchestrator serves as an intermediary that coordinates between the physician's decisions and the automated agent execution. It manages the workflow by selecting appropriate agents based on procedural context, ensuring both automation precision and physician oversight to maintain outcome consistency.
2Reliability
If automated agent selection is implemented using machine learning models, then procedural accuracy and outcome consistency improve, but system complexity increases
Solution Approach 1:
The agent orchestrator is designed as a universal system that can select and coordinate multiple different types of agents through a single standardized interface. The machine learning model serves as a multi-functional component that handles agent selection, workflow management, and fallback protocol activation, reducing overall system complexity despite the presence of multiple specialized agents.
Solution Approach 2:
The system implements feedback mechanisms where the orchestrator continuously monitors agent performance and procedural outcomes. This feedback is used by the machine learning model to refine agent selection decisions, improving reliability while managing complexity through adaptive learning rather than rigid complex rule sets.
3Manufacturing precision
If multiple agents are coordinated through an orchestrator, then procedural accuracy improves, but the time required for data processing and decision-making increases
Solution Approach 1:
Agents are pre-configured and trained before procedure execution. The machine learning model is pre-trained on extensive electrophysiology data to enable rapid agent selection during procedures. Data acquisition agents continuously collect and pre-process information in the background, so when decision points are reached, processed data is immediately available, reducing actual procedural delays.
Solution Approach 2:
The agent coordination system is dynamic and adaptive. The orchestrator adjusts the level of agent involvement and data processing intensity based on procedural context and urgency. For routine steps, automated agents operate with standard processing speeds, while for critical decisions, the system dynamically prioritizes and accelerates relevant data processing streams to minimize time loss.
Data Source
AI summary
Described herein are systems and methods for responding to user input using an agent orchestrator. A system may include a cardiac catheter, a user interface, and a computing device configured to receive, from the user interface, a first user input, receive, from the catheter, the procedure data, determine, using an agent orchestrator, a first agent selection datum, wherein determining the first agent selection datum includes generating the first agent selection datum as a function of the first user input using a trained agent selection machine learning model, using a first agent corresponding to the first agent selection datum, determine a first agent output, wherein determining the first agent output includes inputting into the first agent the procedure data, and receiving, as an output from the first agent, the first agent output, and display, using the user interface, the first agent output.