AI Operating Room Scheduling Model for Resource Optimization

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Solution Overview

Problem

Current methods for creating a case mix schedule for operating rooms rely heavily on experience and are often inefficient, leading to suboptimal resource utilization and a cumbersome scheduling process.

Innovation Solution

A computer-implemented method using a digital operating room model that incorporates historical patient data, medical procedure data, and operating room sensor data to generate a case mix schedule, optimizing resource allocation and scheduling efficiency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If case mix schedule is created based on experience and historical documentation, then scheduling flexibility and surgeon preferences can be accommodated, but resource utilization efficiency deteriorates and scheduling process becomes cumbersome

Engineering Contradiction:

Improvescheduling flexibilityVSAvoidresource utilization efficiency

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The system implements continuous feedback loops where scheduling outcomes, resource utilization metrics, and operational data are fed back into the AI model to continuously optimize future scheduling decisions. This allows the system to learn from past performance and improve resource allocation while maintaining scheduling flexibility through iterative refinement.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The AI model dynamically adjusts scheduling parameters such as case mix ratios, surgeon allocation, and operating room utilization based on real-time data and historical patterns. By changing these parameters optimally, the system achieves both high resource utilization and scheduling flexibility simultaneously.

Inventive Principle:

Principle #35Parameter changes

2Adaptability or versatility

If case mix schedule is created based on experience and historical documentation, then surgeon preferences and historical reasons can be considered, but the scheduling process complexity increases

Engineering Contradiction:

Improvesurgeon preference accommodationVSAvoidscheduling process complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The AI-based scheduling system operates autonomously to generate optimized schedules without requiring complex manual coordination. The system self-adjusts to accommodate surgeon preferences by learning from historical data and automatically incorporating these preferences into the optimization algorithm, thereby reducing process complexity while maintaining adaptability.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The unified AI scheduling platform performs multiple functions including preference analysis, resource optimization, conflict resolution, and schedule generation in a single integrated system. This multi-functional approach consolidates what would otherwise require multiple separate processes, reducing overall complexity while maintaining comprehensive adaptability.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Adaptability or versatility

If traditional experience-based scheduling is used, then established surgeon preferences can be maintained, but computational efficiency and scheduling accuracy deteriorate

Engineering Contradiction:

Improvesurgeon preference maintenanceVSAvoidscheduling accuracy

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The patent replaces traditional experience-based mechanical scheduling with an AI-based intelligent system that processes data computationally. This substitution maintains surgeon preference accommodation through learned patterns while dramatically improving scheduling accuracy through data-driven optimization and predictive analytics.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The AI model acts as an intermediary between surgeon preferences and scheduling decisions, translating qualitative preferences into quantitative optimization parameters. This intermediary layer maintains the essence of surgeon preferences while enabling precise, data-driven scheduling decisions that improve overall accuracy.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20250166801A1Computer implemented method for providing a case mix schedule

Publication Date: 2025.05.22 DEO NV

AI summary

A computer implemented method for providing a case mix schedule for patients in at least one operating room includes providing an operating room model including historical patient data comprising at least one patient parameter related to the patient; medical procedure data comprising at least a procedure identifier indicative for the type of procedure and a procedure resource parameter being indicative of the resource requirement of a medical procedure, where the procedure resource parameter is associated with at least one patient parameter; historical operating room sensor data containing at least one sensor parameter measured during a medical procedure associated with at least one patient parameter and/or procedure identifier, providing patient data for the patients; providing operating room sensor data comprising at least one sensor parameter; and providing the case mix schedule for the patients on the basis of the operating room model, the patient data and the operating room sensor data.