AI Anesthetic Planning Tool for Real-Time Scheduling Optimization

Solution Overview

Problem

Current manual systems for anesthetic planning and scheduling in surgical procedures are inefficient, leading to suboptimal and non-personalized plans, which can increase patient risk and reduce the effectiveness of medical professionals.

Innovation Solution

An AI and machine learning-based method that utilizes trained and crowd-sourced data to provide predictive capabilities for scheduling medical services and treatments, allowing for real-time updates and adjustments in anesthetic plans, incorporating past medical and surgical history, and drug interactions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual systems are used for anesthetic planning and scheduling, then device complexity is reduced, but productivity and planning precision deteriorate

Engineering Contradiction:

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent replaces manual mechanical scheduling systems with an AI-based automated system that uses machine learning models to generate anesthetic plans and schedules. The system processes patient data, surgical schedules, and resource availability through computational algorithms, eliminating manual coordination while improving efficiency and reducing errors.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The AI system performs self-optimization by continuously learning from past surgical outcomes and automatically adjusting its predictive models. The system independently generates personalized anesthetic plans based on patient history, surgical type, and resource constraints without requiring constant human intervention, thereby maintaining high productivity while managing complexity through automation.

Inventive Principle:

Principle #25Self-service

2Measurement precision

If manual scheduling is used, then ease of operation is improved, but measurement precision and planning accuracy deteriorate

Engineering Contradiction:

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The system incorporates feedback loops that continuously monitor surgical outcomes, complications, and resource utilization. This feedback is fed back into the machine learning models to refine future predictions and improve scheduling accuracy. The feedback mechanism enables the system to learn from past performance and progressively improve its measurement precision without requiring manual verification of each scheduling detail.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent introduces an intermediary layer between manual operations and scheduling decisions - an AI mediator that processes raw data, applies complex decision logic, and generates optimized schedules. This intermediary handles the complexity of coordinating multiple constraints and priorities, translating them into actionable schedules while maintaining high accuracy without burdening users with manual coordination tasks.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Manufacturing precision

If AI-based predictive capabilities are implemented, then manufacturing precision (planning precision) is improved, but device complexity increases

Engineering Contradiction:

Core Design Contradiction:

Manufacturing precisionVSDevice complexity

Solution Approach 1:

The AI system is segmented into distinct functional modules: data collection modules for gathering patient history and surgical information, predictive modeling modules for generating anesthetic plans, optimization modules for scheduling, and feedback modules for continuous improvement. This segmentation allows each module to be developed, tested, and maintained independently, managing overall system complexity while achieving high planning precision through coordinated operation of specialized components.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS11823790B2AI driven anesthetic planning, management and case scheduling tool for anesthesiologists, resident physicians, surgeons, proceduralists, mid-level providers and operating room managers (AISCHPLAN)

Publication Date: 2023.11.21

AI summary

In an embodiment, a method includes accessing, by a processor configured within a device, a central system that provides trained data and crowd-sourced data to be used to schedule a plurality of medical services and treatment options. A module is configured to receive the trained data and crowd-sourced data from the central system, and pass the trained data and crowd-sourced data to an application, and provide predictive capabilities to the application based on the trained data and the crowd-sourced data. The processor uses the application to provide scheduling intervals for a series of medical services and treatments at one or more medical stations in real-time. Another device is connected to the first device, wherein the other device is configured to access the application and identify the scheduling intervals, and request updates and changes to the scheduling intervals in real-time.