Clinical Document Sectionalization for Patient Health Records
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Solution Overview
Problem
Current systems face challenges in extracting meaningful insights from electronic medical records due to the narrative form of clinical information, and there are technical and regulatory barriers to accessing and processing these records in a patient-centric manner.
Innovation Solution
A medical information analysis platform that uses machine learning algorithms to process clinical documents, sectionalize them into structured sections, and extract clinical entities, allowing for the generation and storage of structured clinical data records in a searchable format, while also enabling patient-initiated information retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If clinical information is stored in narrative form in doctors reports, then the information can be captured in a flexible and comprehensive manner, but it becomes challenging to extract and analyze meaningful clinical insights
Solution Approach 1:
The patent segments clinical documents into distinct sections (e.g., patient demographics, clinical findings, diagnoses, treatments) and further divides them into structured fields. This segmentation transforms the unstructured narrative form into organized, extractable data elements, enabling efficient information retrieval while managing processing complexity through systematic decomposition of the document structure.
Solution Approach 2:
The patent introduces an intermediary processing layer that includes natural language processing algorithms and structured data models. This intermediary transforms the narrative clinical text into standardized structured formats, bridging the gap between flexible narrative storage and systematic information extraction, thereby reducing the complexity of direct analysis while preserving information integrity.
2Reliability
If privacy restrictions are imposed on medical records, then patient confidentiality is protected, but accessing and processing these records for research and treatment becomes more difficult
Solution Approach 1:
The patent extracts only the necessary clinical information needed for treatment and research purposes while leaving the remaining sensitive data protected. By selectively extracting specific clinical entities and structured data elements rather than accessing entire medical records, the system maintains patient confidentiality while enabling useful information retrieval and analysis.
3Productivity
If electronic medical records are computerized for streamlined operations, then office functions are improved, but the ability to derive meaningful clinical insights from the data is limited
Solution Approach 1:
The patent changes the structural parameters of electronic medical records from simple stored procedures to sophisticated structured formats with defined fields, hierarchies, and relationships. This parameter transformation enables both efficient computerized processing and advanced analytical capabilities, allowing the system to maintain productivity benefits while unlocking deeper clinical insights through structured data analysis.
Data Source
AI summary
Techniques for analyzing patient health records are provided. Clinical documents may be received in response to a patient-initiated request, for example. In one embodiment, machine learning algorithms are used to sectionalize and extract data from clinical documents. The machine learning algorithms used may be more highly focused for analyzing text residing deeper in a clinical document hierarchy, for example. In one embodiment, extracted data is stored in a patient graph. Searches may be made against the graph to yield results to help save lives and/or improve patient outcomes.