Operating Room Surgical Milestone Detection and Schedule Adjustment
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Solution Overview
Problem
Current surgical scheduling and resource management in operating rooms often rely on manual methods, such as whiteboards, which are inefficient and inaccurate, diverting staff attention from patient care and complicating the management of surgical schedules and resources.
Innovation Solution
A system that uses cameras and trained machine-learning models to monitor operating rooms, detect surgical milestones, and adjust surgical schedules by receiving images, identifying medical resources, and generating alerts for staff, thus improving scheduling and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual scheduling methods (whiteboards, grease boards) are used, then staff can directly manage and update schedules, but scheduling efficiency is low and staff attention is diverted from patient care
Solution Approach 1:
The system enables self-service scheduling by automatically detecting surgical milestones through image analysis and adjusting schedules without manual intervention. Cameras capture operating room scenes, machine learning models identify surgical milestones, and the system automatically updates schedules and alerts staff, eliminating the need for manual schedule management while keeping staff focused on patient care.
2Measurement precision
If manual scheduling methods are used, then flexibility in managing schedule changes is maintained, but accuracy in predicting schedule changes and resource planning is poor
Solution Approach 1:
The patent replaces manual mechanical scheduling methods with an automated optical and computational system. Cameras capture visual information, machine learning models process images to detect surgical milestones, and algorithms automatically adjust schedules. This substitution of manual mechanical operations with automated sensing and computation systems significantly improves schedule prediction accuracy while managing complexity through specialized AI models.
3Productivity
If automated image-based milestone detection is implemented, then scheduling accuracy and resource management improve, but system complexity and initial setup requirements increase
Solution Approach 1:
The system achieves multi-functionality by using a single integrated platform that performs multiple tasks: capturing images via cameras, detecting surgical milestones through machine learning, adjusting surgical schedules, allocating medical resources, and sending alerts to staff. This universal system consolidates what would otherwise require multiple separate tools and processes, improving resource management efficiency while managing overall system complexity through integration.
Data Source
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Figure 3A~3B
AI summary
The present disclosure relates generally to improving operating room scheduling and resource management efficiency, and more specifically to techniques for monitoring various aspects of an operating room. An exemplary method for adjusting a surgical schedule specifying timing information for one or more surgeries to occur in an operating room comprises: receiving one or more images of the operating room captured by one or more cameras; detecting one or more surgical milestones of a plurality of predefined surgical milestones using one or more trained machine-learning models based on the received one or more images, wherein the one or more machine learning models are trained using a plurality of training images depicting the one or more surgical milestones; and adjusting, based on the detected one or more surgical milestones, the surgical schedule specifying timing information for the one or more surgeries to occur in the operating room.