Surgical Training System Identifying Influential Surgeons
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Solution Overview
Problem
Current surgical training systems lack an efficient method to identify and prioritize surgeons who can effectively disseminate optimal surgical techniques, leading to suboptimal skill transfer and training efficiency.
Innovation Solution
A surgical training system that analyzes surgical characteristics and network influence to identify key surgeons, using data from multiple surgeries and machine learning algorithms to predict outcome scores and propagate best practices through the surgical network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional surgical training methods are used without identifying key influencers, then training coverage is broad but training efficiency and skill transfer are suboptimal
Solution Approach 1:
The patent introduces data analytics and machine learning algorithms as intermediaries between surgical procedures and training outcomes. These intermediaries process surgical characteristic data, identify influential surgeons through network analysis, and propagate best practices systematically, thereby enhancing both training efficiency and skill transfer effectiveness without requiring direct broad-based training of all surgeons
Solution Approach 2:
The patent replaces the mechanical traditional approach of broad surgical training with an information-based system using data capture, analysis, and propagation. By substituting physical training interactions with data-driven identification and targeted dissemination of best practices through influential surgeons, the system achieves higher efficiency and better skill transfer
2Measurement precision
If surgical characteristic data is collected and analyzed, then surgeon skill levels can be recognized, but the ability to determine optimal training targets is insufficient
Solution Approach 1:
The patent implements feedback loops where surgical characteristic data is continuously captured, analyzed, and used to update the identification of influential surgeons and optimal training targets. The system learns from outcomes and refines its ability to match training needs with appropriate influencers, enhancing both measurement precision and adaptability over time
Solution Approach 2:
The patent dynamically adjusts training parameters by changing which surgeons are identified as targets based on evolving surgical characteristic data and network influence metrics. The system adapts its selection criteria and weighting parameters to optimize training target identification as more data becomes available
3Quantity of substance
If all surgeons receive training, then coverage is maximized, but resource utilization and training impact are suboptimal
Solution Approach 1:
The patent extracts and isolates the most influential surgeons from the broader surgical population using data analysis and network metrics. By taking out these key influencers for targeted training, the system achieves maximum training impact with optimized resource utilization, while still ensuring broader coverage through the influencers' subsequent dissemination of learned practices
Data Source
AI summary
A surgical training system comprising circuitry configured to: obtain surgical information recorded during each of a plurality of surgical performances occurring at a plurality of identified times by each of a plurality of surgeons in a surgeon network; determine a level of influence of each surgeon using the surgical information and the identified times; and output an identifier of a surgeon with a level of influence which meets a predetermined condition as a candidate for receiving training.