Unsupervised AI for Patient-Level Clinical Event Extraction
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Solution Overview
Problem
Existing methods for processing unstructured clinical documentation are error-prone, time-consuming, and expensive, and fail to provide a comprehensive patient view, especially when dealing with diverse clinical domains and styles of documentation, requiring substantial effort to adapt to new data and retrain models.
Innovation Solution
An unsupervised AI model is used to automatically abstract and align clinical facets by converting unstructured clinical documentation to vector representations, encoding them using a deep learning model like BERT, and adjusting the model with a QA schema to enhance adaptability and resilience to new concepts and domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual abstraction approach is used to process unstructured clinical documentation, then data can be populated on a timeline, but the process is error-prone and expensive
Solution Approach 1:
The patent replaces the manual mechanical abstraction process with an automated AI-based system that uses natural language processing to extract clinical events from unstructured documentation. The system converts unstructured text into structured data automatically, eliminating human labor while maintaining or improving extraction accuracy through machine learning models.
2Reliability
If supervised AI models are trained on specific training data, then the model can solve well-defined problems, but the model does not work well on different styles of data or new clinical domains
Solution Approach 1:
The patent creates a universal AI system that can handle multiple clinical domains and documentation styles through a unified architecture. The system uses a combination of pre-trained language models and domain-specific training that allows it to adapt to different clinical areas (oncology, family medicine, etc.) and documentation styles without requiring complete retraining, making it multi-functional across diverse healthcare contexts.
Solution Approach 2:
The system employs dynamic adaptation mechanisms where the AI model can be fine-tuned for specific domains while maintaining its core capabilities. The architecture allows for flexible adjustment of model parameters and training data weights, enabling the system to dynamically adapt to new clinical domains and documentation styles as needed.
3Measurement precision
If traditional supervised AI models are used, then the model can be trained on labeled data, but substantial manual effort is required to label data for training
Solution Approach 1:
The patent implements self-service mechanisms where the AI system automatically generates training data through weak supervision and automated labeling techniques. The system uses rules-based approaches, pattern recognition, and semi-automated processes to create labeled training datasets without requiring extensive manual annotation, significantly reducing the time and resources needed for data preparation.
4Loss of information
If manual abstraction is used to process clinical documentation, then data can be extracted, but the process is extremely time consuming and does not scale to large populations
Solution Approach 1:
The patent replaces manual abstraction with automated AI processing that can handle large volumes of clinical documentation simultaneously. The system uses parallel processing and scalable infrastructure to process thousands of patient records concurrently, maintaining extraction completeness while achieving high throughput that scales to large population studies.
Data Source
AI summary
Some embodiments of the present disclosure provide a framework for using unsupervised artificial intelligence to automatically abstract and align clinical facets. The approach of the present application may be shown to reduce human involvement and, accordingly, enhance privacy compliance. Aspects of the present application relate to a process of self-learning from the data available. Accordingly, aspects of the present application may be shown to be resilient to the appearance of new concepts and facets in future data. Additionally, aspects of the present application may be shown to adapt well when presented with different languages, different styles of documentation and different clinical domains. Aspects of the present application relate to processing unstructured, non-fielded data, such as clinical notes, admission and discharge summaries, surgical notes, lab reports and imaging reports. These notes may be considered to contain hidden insights in the clinical domain. Additionally, these notes may be considered to contain data that may not be captured elsewhere in a readily usable way. Aspects of the present application may be shown to support analysis of large size populations at a relatively low incremental cost.