Clinical Decision Support Analytics Engine for Content Harmonization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Clinical decision support content used by medical providers is often outdated and not aligned with recent developments, such as FDA safety alerts and new clinical guidelines, leading to suboptimal treatment practices.
Innovation Solution
A system and process that analyze clinical decision support content against reference content, identifying deficiencies and generating reports and recommendations to ensure the content is current and harmonized across a medical organization, utilizing an analytics engine with modules for text extraction, entity alignment, pattern matching, and machine learning to assess and update clinical guidelines, care plans, and order sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If clinical decision support content is manually updated and reviewed, then content accuracy can be maintained, but the content becomes outdated quickly due to the time-consuming nature of manual updates
Solution Approach 1:
The patent replaces manual mechanical review processes with automated text extraction, entity alignment, and pattern matching systems. The analytics engine automatically compares CDS content against reference materials, eliminating the need for time-consuming manual updates while maintaining accuracy through systematic automated analysis.
Solution Approach 2:
The system enables self-service by allowing the analytics engine to autonomously identify deficiencies in CDS content by comparing it with reference materials. The system automatically generates reports and recommendations without requiring continuous manual intervention, making the content maintenance process self-sustaining.
2Loss of information
If comprehensive reference content is used to evaluate CDS documents, then content completeness improves, but the complexity of the analysis system increases
Solution Approach 1:
The patent segments the analysis process into distinct modules: text extraction module, entity alignment module, and pattern matching engine. Each module handles a specific aspect of the analysis, making the complex system manageable through functional decomposition while comprehensively evaluating CDS content against reference materials.
Solution Approach 2:
The analytics engine serves multiple functions simultaneously: extracting text, aligning entities, matching patterns, and generating reports. This multi-functionality reduces the need for separate specialized systems while maintaining comprehensive content evaluation capabilities.
3Productivity
If automated text extraction and entity alignment are performed, then analysis speed increases, but the precision of matching terminology decreases when terminology differs between documents
Solution Approach 1:
The entity alignment module acts as an intermediary between text extraction and pattern matching. It bridges terminology differences by aligning entities from CDS content with corresponding entities in reference materials, enabling accurate matching even when terminology differs between documents while maintaining high analysis speed.
4Reliability
If real-time analysis of CDS content is implemented, then content currency is improved, but the computational resources required increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and indexing reference materials before they are needed for comparison. This preparation work is done in advance, reducing the computational burden during real-time CDS content analysis while maintaining the ability to provide timely updates.
Data Source
AI summary
Clinical content analytics engines and associated processes are described. An engine receives a clinical decision support document, accesses corresponding reference content, identifies and extracts medical intervention content from the clinical decision support document, segments extracted medical intervention content into a first plurality of segments including at least a first segment comprising a first set of text, determines if the first segment corresponds to at least a first item included in the reference content, the first item comprising a second set of text comprising terminology different than that found in the first set of text, and in response to determining that the first segment corresponds to the first item included in the reference content, causing a report to include an indication that the first segment corresponds to the first item included in the reference content.