Surgical Duration Extraction From OR Data and Non-Surgical Events
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Solution Overview
Problem
Conventional methods for measuring surgical procedure duration in operating rooms (ORs) fail to distinguish between actual surgical time and non-surgical events, leading to inaccurate assessments of surgeon and OR efficiency due to inclusion of support team inefficiencies in total OR time measurements.
Innovation Solution
A surgical procedure analysis system that utilizes machine-learning modules to analyze multiple data sources, including endoscope videos, OR videos, pressure sensors, and audio recordings, to identify and categorize non-surgical events, allowing extraction of actual surgical duration by subtracting these events from total OR time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If total OR time is used to measure surgical procedure duration, then ease of measurement is improved, but measurement precision deteriorates due to inclusion of non-surgical events
Solution Approach 1:
The patent segments the total OR time into distinct components: actual surgical time and non-surgical events. By dividing the continuous time measurement into discrete segments (surgical events, tool exchanges, timeouts, patient preparation), the system enables precise measurement of only the actual surgical duration while separately tracking and excluding non-surgical activities.
Solution Approach 2:
The patent extracts non-surgical events from the total OR time measurement. Using machine learning analysis of video, audio, and sensor data, the system identifies and removes time periods corresponding to tool exchanges, surgical timeouts, and patient preparation activities, leaving only the pure surgical duration for accurate surgeon efficiency evaluation.
2Measurement precision
If multiple data sources are analyzed to identify non-surgical events, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent employs a multi-functional analysis system that processes multiple data types (video feeds, audio recordings, sensor data) through a unified machine learning framework. The same computational infrastructure analyzes all data sources simultaneously, identifying non-surgical events through pattern recognition across modalities, thereby achieving high measurement precision without proportionally increasing system complexity.
Solution Approach 2:
The patent introduces machine learning algorithms as an intermediary layer between raw multi-source data and surgical duration measurement. This intermediary automatically processes and integrates video, audio, and sensor information, translating complex multi-modal inputs into accurate identification of non-surgical events without requiring direct manual analysis of each data source.
3Measurement precision
If non-surgical events are excluded from total OR time, then measurement precision is improved, but loss of information increases
Solution Approach 1:
The patent extracts non-surgical events from the total OR time while preserving their separate identification and classification. Rather than simply discarding this information, the system categorizes excluded events (tool exchanges, timeouts, preparation activities) into distinct types, maintaining a complete record of all OR activities while enabling precise measurement of actual surgical duration for efficiency evaluation.
Data Source
AI summary
Embodiments described herein provide various examples of a system for extracting an actual procedure duration composed of actual surgical tool-tissue interactions from an overall procedure duration of a surgical procedure on a patient. In one aspect, the system is configured to obtain the actual procedure duration by: obtaining an overall procedure duration of the surgical procedure; receiving a set of operating room (OR) data from a set of OR data sources collected during the surgical procedure, wherein the set of OR data includes an endoscope video captured during the surgical procedure; analyzing the set of OR data to detect a set of non-surgical events during the surgical procedure that do not involve surgical tool-tissue interactions; extracting a set of durations corresponding to the set of non-surgical events; and determining the actual procedure duration by subtracting the set of extracted durations from the overall procedure duration.