Surgical Video Analysis for Insurance Reimbursement Coding
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Solution Overview
Problem
Existing surgical video analysis systems lack efficient and effective methods for decision support, postoperative analysis, and surgical event identification, which hinders surgeons' preparation and performance.
Innovation Solution
A system utilizing specialized hardware and software for analyzing surgical videos, incorporating surgical timelines, video indexing, and generating summaries to provide decision support, identify surgical phases and events, and facilitate postoperative activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If surgical videos are analyzed manually to identify surgical events and phases, then measurement precision can be achieved, but loss of time increases significantly
Solution Approach 1:
The patent replaces manual mechanical review of surgical videos with an automated computer vision system that uses machine learning algorithms to detect, classify, and timestamp surgical events and phases, dramatically reducing analysis time while maintaining or improving identification accuracy
Solution Approach 2:
The system introduces an intermediary automated analysis layer between the raw surgical video and the surgeon, using trained models to interpret video content and generate structured event annotations, thereby eliminating the need for time-consuming manual review
2Reliability
If comprehensive video footage is reviewed to ensure complete documentation, then reliability of surgical record is improved, but loss of time increases due to reviewing entire videos
Solution Approach 1:
The system extracts only the relevant surgical events and phases from the complete video footage, automatically identifying and isolating key moments such as incisions, instrument usage, and critical steps, allowing reviewers to focus on essential content rather than watching entire hours of video
Solution Approach 2:
The surgical video is automatically segmented into distinct phases and events with temporal boundaries, creating a structured breakdown that allows comprehensive documentation review to be performed efficiently by navigating through labeled segments rather than continuous footage
3Measurement precision
If detailed analysis of all video frames is performed to identify all surgical events, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The analysis system is divided into specialized modules that handle different aspects of video analysis independently - object detection, event classification, phase identification, and temporal annotation - allowing each component to be optimized separately while maintaining overall system accuracy
Solution Approach 2:
The system employs multi-functional machine learning models that can perform multiple analysis tasks simultaneously, such as detecting both surgical instruments and anatomical structures in the same video frame, reducing the need for separate specialized systems
Data Source
AI summary
Systems and methods for determining insurance reimbursement are disclosed. A system may include at least one processor configured to access video frames captured during a surgical procedure on a patient and analyze the video frames to identify a medical instrument, an anatomical structure, and an interaction between the medical instrument and the anatomical structure. The processor may access a database of reimbursement codes correlated to medical instruments, anatomical structures, and interactions between medical instruments and anatomical structures and compare the identified interaction between the medical instrument and the anatomical structure with information in the database of reimbursement codes to determine a reimbursement code associated with the surgical procedure and output the reimbursement code for use in obtaining an insurance reimbursement for the surgical procedure.