Dynamic Medical Workflow Dashboard Using Machine Learning
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Solution Overview
Problem
Existing operating room dashboards present static content, which can hinder medical staff efficiency and safety due to the need for manual updates and the presence of idle or inexperienced staff, leading to increased surgical procedure durations and negative impacts on patient care.
Innovation Solution
Implementing machine learning models to intelligently identify the current stage of a medical workflow and dynamically update content presented to medical staff, including contextual information and task guidance, to optimize resource allocation and improve efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static content is presented on operating room dashboards with manual updates, then information accuracy can be maintained through human input, but medical staff efficiency decreases due to idle time and inability to perform tasks proactively
Solution Approach 1:
The system enables the dashboard to automatically update itself by detecting surgical stages and retrieving relevant content without requiring manual input from medical staff. The processor automatically monitors the surgical environment, identifies stage transitions, and updates the display, allowing the system to serve itself rather than relying on human operators for each update.
Solution Approach 2:
The patent replaces the manual mechanical process of staff updating dashboard content with an automated electronic system. Machine learning models and image processing algorithms substitute for human judgment and action, automatically detecting surgical stages from video feeds and retrieving appropriate content from databases, thereby eliminating the need for manual intervention.
2Reliability
If experienced medical staff provide instructions or perform tasks for idle/inexperienced staff, then task completion is ensured, but surgical procedure duration increases and overall efficiency decreases
Solution Approach 1:
The system performs preliminary actions by proactively detecting when a surgical stage is occurring and automatically displaying the relevant task checklist and instructions before the medical staff need to perform those tasks. This advance preparation ensures that when tasks need to be completed, the information is already available, eliminating delays and the need for experienced staff to intervene and provide instructions.
3Productivity
If dynamic content updating is implemented using machine learning models, then medical staff efficiency improves through automated information delivery, but system complexity increases
Solution Approach 1:
The system achieves multi-functionality by using a single integrated platform that combines video feed analysis, machine learning stage detection, database querying, and content display functions. The processor performs multiple tasks - detecting surgical stages from video, retrieving relevant content from databases, and updating displays - all within one system, reducing the need for multiple separate complex systems.
Data Source
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AI summary
Disclosed herein are methods, systems, and programming for evaluating a medical workflow. For example, one or more images depicting a medical environment and a medical workflow performed in the medical environment may be received. Contextual information associated with the medical workflow may be determined, and a stage of the medical workflow may be identified, based on the one or more images. A subsequent stage of the medical workflow may be determined based on the identified stage, and first medical content associated with the contextual information and second medical content associated with the subsequent stage may be presented to one or more medical staff located within the medical environment.