Weighted Clinical Knowledge Graphs for Automated Scribe Documentation
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Solution Overview
Problem
The increasing demand for granular, codified, and structured clinical data has led to a significant burden on healthcare providers due to complex software and data entry systems, reducing the joy of practicing medicine and increasing frustration, while semi-automated data capture systems require a clinical semantic network for effective dialog and response interpretation.
Innovation Solution
A knowledge graph of clinical information is generated to facilitate automated scribe technology, leveraging machine learning and natural language processing to automate clinical documentation, reducing administrative burden and improving physician efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If complex software and data entry systems are used to capture granular clinical data, then data structure and completeness are improved, but provider burden and frustration increase
Solution Approach 1:
The system enables automated clinical documentation where the software automatically captures, structures, and codes clinical data during patient encounters without requiring manual provider input. The ambient clinical intelligence system listens to provider-patient conversations and autonomously generates structured documentation, allowing the system to serve itself rather than requiring provider labor for data entry.
Solution Approach 2:
The patent replaces manual mechanical data entry processes with automated speech recognition and natural language processing systems. The ambient clinical intelligence system substitutes the mechanical act of typing and form-filling with automated audio capture and AI-driven documentation generation, eliminating the need for providers to manually interact with complex data entry interfaces.
2Productivity
If semi-automated data capture systems are implemented, then data collection efficiency is improved, but the requirement for clinical semantic network complexity increases
Solution Approach 1:
The patent introduces a clinical semantic network as an intermediary layer between speech recognition and structured data output. This semantic network, built from clinical guidelines and knowledge graphs, mediates the translation of spoken clinical language into structured documentation, allowing the system to handle clinical complexity without requiring providers to directly manage the complexity.
Solution Approach 2:
The system performs preliminary action by pre-building comprehensive clinical semantic networks and knowledge graphs from existing clinical guidelines, textbooks, and medical literature before patient encounters. This preparatory work allows the automated documentation system to efficiently process clinical data during encounters without requiring complex real-time processing, as the semantic framework is already in place.
3Loss of time
If automated scribe technology is used to reduce administrative burden, then provider time is improved, but system implementation complexity increases
Solution Approach 1:
The ambient clinical intelligence system performs multiple functions within a single integrated platform: speech recognition, natural language processing, clinical decision support, documentation generation, and data structuring. This multi-functionality consolidates what would otherwise require multiple separate systems into one unified solution, reducing overall system complexity while maintaining comprehensive capabilities.
Data Source
AI summary
Method, system, device, and non-transitory computer-readable medium for generating a knowledge graph of clinical information. In some examples, a computer-implemented method includes: identifying a clinical encounter associated with a clinical concern; representing the clinical concern as a module of a knowledge graph of clinical information; associating at least one section node with the module, each section node of the at least one section node corresponding to a clinical concept relevant to the clinical concern; associating, for each section node of the at least one section node, at least one topic node with the section node, each topic node of the at least one topic node corresponding to a clinical topic relevant to the corresponding clinical concept; and generating the knowledge graph of clinical information to represent the clinical concern, the relevant clinical concepts, and the relevant clinical topics.