Interconnected Surgical Data Structures for Accurate Procedure Notes
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Solution Overview
Problem
Traditional methods of preparing operational notes during surgeries are prone to inaccuracies and omissions due to discrepancies in terminology and background knowledge between the author and physician, leading to potential errors in patient care and increased healthcare costs.
Innovation Solution
A computer-implemented method using multiple interconnected surgical data structures and multi-dimensional artificial intelligence to process surgical data streams, identifying procedural states and generating electronic outputs that accurately represent surgical procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual preparation of operational notes is used, then the process is simple and direct, but accuracy and completeness of the notes deteriorate due to terminology discrepancies and knowledge gaps between author and physician
Solution Approach 1:
The patent introduces an intermediary natural language processing system that acts as a mediator between the surgical audio data and the operational note generation. This NLP intermediary processes the audio streams, identifies surgical events, and generates structured notes, eliminating the need for manual transcription while ensuring accuracy through automated terminology standardization and context understanding.
Solution Approach 2:
The patent replaces the mechanical manual process of note preparation with an automated computational system. Instead of a human author manually transcribing and organizing surgical events, the system uses audio processing, natural language generation, and automated structuring algorithms to produce operational notes, thereby improving accuracy while reducing human error and variability.
2Reliability
If automated processing of surgical data is implemented, then accuracy and completeness of operational notes improve, but the complexity of the processing system increases
Solution Approach 1:
The patent segments the complex surgical data processing task into multiple independent components: audio stream processing, surgical event detection, terminology standardization, note generation, and quality verification. Each component is handled by a specialized module within the system, allowing for improved reliability through focused functionality while managing overall system complexity through modular architecture.
Solution Approach 2:
The patent creates a universal processing framework that handles multiple types of surgical data (audio streams, video feeds, device data) through a single integrated system. The natural language processing engine and data structure generation mechanisms serve multiple functions including event detection, terminology normalization, and note generation, thereby improving reliability through consistent processing while avoiding the need for separate specialized systems.
3Measurement precision
If multiple interconnected data structures are used to process surgical data, then the precision and accuracy of surgical procedure representation improve, but the complexity of data processing increases
Solution Approach 1:
The patent transitions from traditional linear text-based operational notes to a multi-dimensional data structure that incorporates temporal, spatial, and hierarchical dimensions. Surgical events are represented in a structured format that captures not only what occurred but when, where, and in what context, thereby improving precision of surgical state identification while organizing complexity into manageable dimensional categories.
Solution Approach 2:
The patent implements dynamic data structures that adapt and evolve throughout the surgical procedure. The system continuously processes incoming surgical data streams and updates the operational note representation in real-time, allowing the data structures to dynamically reflect the current surgical state rather than requiring complete restructuring, thereby improving precision without proportionally increasing complexity.
Data Source
AI summary
The present disclosure relates to processing data streams from a surgical procedure using multiple interconnected data structures to generate and/or continuously update an electronic output. Each surgical data structure is used to determine a current node associated with a characteristic of a surgical procedure and present relevant metadata associated with the surgical procedure. Each surgical data structure includes at least one node interconnected to one or more nodes of another data structure. The interconnected nodes between one or more data structures includes relational metadata associated with the surgical procedure.