Clinical Knowledge Graph for Automated Scribe Documentation
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Solution Overview
Problem
The increasing demand for structured clinical data has led to a significant burden on healthcare providers due to complex data entry systems, hindering personalized care and increasing frustration, as existing technologies fail to automate clinical documentation efficiently.
Innovation Solution
A knowledge graph of clinical information is generated and applied using a computer-implemented method that semantically represents relevant clinical data, leveraging machine learning and natural language processing to automate clinical documentation, thereby reducing provider burden and improving data entry efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If complex software and data entry systems are used to meet the demand for structured clinical data, then data structure and granularity are improved, but provider data entry burden increases
Solution Approach 1:
The system enables automated clinical documentation that self-generates structured data from unstructured clinical notes, eliminating the need for manual data entry by providers. The natural language processing automatically extracts and structures clinical information, allowing the system to serve itself rather than requiring provider intervention.
Solution Approach 2:
The patent replaces manual mechanical data entry processes with automated natural language processing and machine learning systems. The computer vision and NLP technologies automatically convert clinical notes into structured data, substituting the manual typing and form-filling processes with intelligent automated systems.
2Productivity
If manual data entry is used for clinical information, then implementation simplicity is maintained, but productivity and documentation accuracy decrease
Solution Approach 1:
The system performs multiple functions including automated note generation, data extraction, structuring, and quality assessment within a single integrated platform. The knowledge graph technology enables the system to handle various clinical data types and formats universally, improving productivity across different clinical workflows without requiring separate systems.
Solution Approach 2:
The patent introduces an intermediary layer of natural language processing and knowledge graphs between unstructured clinical notes and structured data requirements. This intermediary automatically translates clinical documentation into structured formats, resolving the complexity issue by providing a seamless translation layer that maintains simplicity for providers while achieving high productivity.
3Extent of automation
If automated scribe technology is implemented, then administrative task reduction is achieved, but technology complexity increases
Solution Approach 1:
The system segments the complex automation task into distinct functional modules including speech recognition, natural language processing, knowledge graph construction, and data structuring. This segmentation allows each component to be optimized independently while working together to achieve high-level automation, making the overall system more manageable despite its complexity.
Solution Approach 2:
The patent implements preliminary action by pre-training machine learning models and constructing knowledge graphs before clinical use. The system performs automated note generation and data extraction in advance of actual clinical needs, reducing the complexity burden during active use by having the heavy computational work completed beforehand.
Data Source
AI summary
According to certain embodiments, the present disclosure includes a method for generating a knowledge graph of clinical information for use as a reference model to semantically represent relevant information from clinical encounters. In certain embodiments, the method includes representing a clinical concern as a module of the knowledge graph. In some examples, the method includes associating at least one section node with the module, wherein each section node of the at least one section node corresponds to a clinical concept relevant to the clinical concern and associating at least one topic node with the at least one section node, wherein each topic node of the at least one topic node corresponds to a clinical topic relevant to the clinical concept. And, in certain embodiments the method includes outputting the knowledge graph of clinical information to semantically represent the relevant information from a clinical encounter associated with the clinical concern.