Clinical Documentation Video Snippet Extraction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current automated clinical documentation systems face challenges in efficiently extracting and storing relevant video information from patient encounters, as they often require uploading entire video recordings, which is bandwidth-intensive and raises privacy concerns, while also struggling to accurately identify and isolate important video snippets for documentation purposes.
Innovation Solution
A method that determines the relative importance of words in generated reports based on keywords, user feedback, or fact extraction, and uses attention distribution to identify timestamps of conversational turns, allowing for the storage and display of only relevant video snippets, reducing bandwidth usage and enhancing privacy by uploading only necessary video portions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If entire video recordings are uploaded for clinical documentation, then complete documentation accuracy is achieved, but bandwidth consumption increases and privacy concerns arise
Solution Approach 1:
The patent segments the entire video recording into discrete clips based on identified conversational turns. Only clips containing medically relevant information are selected and uploaded for documentation purposes, rather than uploading the complete video recording. This segmentation approach maintains documentation accuracy while significantly reducing bandwidth consumption.
Solution Approach 2:
The system extracts and isolates specific video clips that contain relevant medical information from the full video recording. By using natural language processing to identify conversational turns and extract only those portions containing clinically significant content, the system achieves accurate documentation with minimal bandwidth usage.
2Reliability
If entire video recordings are uploaded for review, then complete information is available for documentation accuracy, but privacy protection is compromised
Solution Approach 1:
The patent divides the video recording into discrete segments or clips corresponding to specific conversational turns. Only segments containing medically relevant information are selected for upload and review, while other segments remain local. This segmentation protects patient privacy by limiting exposure of non-relevant video portions.
Solution Approach 2:
The system extracts only the necessary video portions that contain clinically relevant information, leaving the rest of the video data local and private. This extraction approach ensures that only essential information is shared for documentation purposes, thereby protecting patient privacy while maintaining documentation accuracy.
3Loss of energy
If video snippets are selectively extracted based on word importance, then bandwidth usage is reduced, but identification accuracy of relevant segments becomes challenging
Solution Approach 1:
The system employs natural language processing and machine learning models that analyze the transcribed audio to identify important words and conversational turns. The feedback from the NLP analysis guides the selection of video clips, ensuring that only segments containing medically relevant information are extracted and uploaded, thereby maintaining identification accuracy while reducing bandwidth usage.
Solution Approach 2:
The patent replaces manual review of entire video recordings with automated natural language processing and machine learning systems. These intelligent systems analyze the transcribed conversation, identify key medical terms and concepts, and automatically select relevant video segments, achieving high identification accuracy without manual intervention and with minimal bandwidth consumption.
Data Source
AI summary
A method, computer program product, and computing system for obtaining, by a computing device, encounter information of a patient encounter, wherein the encounter information may include audio encounter information and video encounter information obtained from at least a first encounter participant. A report of the patient encounter may be generated based upon, at least in part, the encounter information. A relative importance of a word in the report may be determined. A portion of the video encounter information that corresponds to the word in the report may be determined. The portion of the video encounter information that corresponds to the word in the report may be stored at a first location, wherein the video encounter information may be stored at a second location remote from the first location.