ML-Based Operating Room Efficiency Tracking
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Solution Overview
Problem
Traditional methods for measuring efficiency and utilization in hospital operating rooms are inefficient, often requiring manual data entry and specialized hardware, leading to inaccurate data and increased costs.
Innovation Solution
A system that uses machine learning models to automatically generate context assessments based on images and telemetry streams from existing devices, such as cameras and microphones, to track patient presence and procedure stages, reducing the need for additional hardware and manual data entry.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data entry and specialized hardware are used to track operating room efficiency, then measurement capability is provided, but device complexity and cost increase
Solution Approach 1:
The patent applies universality by enabling existing operating room cameras and microphones to perform multiple functions: their primary function for surgical documentation is maintained, while a secondary function for efficiency measurement is added through machine learning analysis of video and audio data. This eliminates the need for specialized tracking hardware.
Solution Approach 2:
The system applies self-service by having the existing imaging and audio equipment automatically generate efficiency data through integrated machine learning models. The cameras and microphones capture data that is automatically analyzed to determine patient presence, provider activities, and procedure milestones without requiring manual data entry or additional specialized sensors.
2Loss of information
If manual data entry is required for efficiency tracking, then data collection is possible, but productivity decreases due to additional staff burden
Solution Approach 1:
The system applies self-service by automatically generating efficiency metrics from existing video and audio feeds through machine learning analysis. The operating room staff continue their primary surgical duties without interruption, while the system autonomously captures and analyzes data to produce completeness-equivalent efficiency information.
Solution Approach 2:
The patent replaces the mechanical process of manual data entry with an automated computational system. Machine learning models analyze video and audio streams to automatically extract efficiency metrics, substituting human labor with algorithmic processing that maintains data completeness while eliminating staff burden.
3Loss of time
If traditional efficiency measurement methods are used, then basic tracking is achieved, but measurement precision deteriorates due to delayed and inaccurate data entry
Solution Approach 1:
The system applies continuity of useful action by continuously analyzing video and audio streams in real-time throughout the procedure. Rather than relying on retrospective manual entry, the machine learning models process data continuously as it is generated, ensuring both timeliness and accuracy of efficiency measurements without interruption to surgical workflow.
Data Source
AI summary
A system and method are provided for performing operations comprising: receiving one or more images from an image capture device of a medical treatment location; applying a trained machine learning model to the one or more images to detect presence of a patient in the medical treatment location, the trained machine learning model being trained to establish a relationship between one or more features of images of the medical treatment location and patient presence; generating context assessment for the medical treatment location based on the detected presence of the patient; and transmitting, over a network, the context assessment for presentation on a user interface of a client device.