Computational Medical Evidence for Reliable Clinical Reasoning
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Solution Overview
Problem
Current medical evidence is predominantly narrative and document-based, leading to inefficiencies in dissemination, delayed implementation, and inability to scale with precision medicine, with existing AI architectures and clinical ontologies failing to provide controllable and reliable representation and reasoning.
Innovation Solution
A computational evidence platform that converts medical evidence into structured computational elements, enabling real-time use and integration with patient data, and allows for inferential reasoning across healthcare stakeholders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If medical evidence is represented as narrative documents and guidelines, then human interpretation and expert assembly are possible, but diffusion, feedback and scalability are severely limited
Solution Approach 1:
The patent replaces the mechanical human interpretation process with an AI-based computational system. The narrative medical evidence is automatically translated into structured computational representations that machines can process, enabling scalable diffusion and feedback mechanisms without requiring human intervention for each evidence item.
Solution Approach 2:
The patent changes the fundamental parameter of evidence representation from unstructured narrative text to structured computational data formats. This transformation enables the evidence to be processed, queried, and integrated by AI systems, achieving scalability while maintaining the essential medical information.
2Reliability
If AI architectures based on text alone are used, then processing medical documents is possible, but controllable and reliable representation and reasoning cannot be achieved
Solution Approach 1:
The patent segments the medical evidence into discrete, structured computational representations (such as knowledge graphs with defined entities and relationships). This segmentation allows AI systems to process and reason about individual evidence components controllably, improving reliability while managing complexity through modular organization.
Solution Approach 2:
The patent introduces an intermediary layer between raw narrative documents and AI processing. This intermediary transforms unstructured text into structured computational representations that serve as a bridge, enabling reliable and controllable AI reasoning while abstracting the complexity of the original documents.
3Ease of operation
If mapping standard ontologies to documents is performed, then meta-data is provided, but controllable use of the information by AI systems is not enabled
Solution Approach 1:
The patent creates a universal computational representation framework that can be used across multiple AI systems and applications. The structured format serves multiple functions: it provides metadata, enables controllable AI processing, maintains context relationships, and facilitates integration across different healthcare systems, eliminating the need for separate representation approaches.
4Productivity
If narrative evidence is boiled down to paper documents requiring human interpretation, then current best practice is maintained, but the process is slow, inefficient and generalized
Solution Approach 1:
The patent substitutes the manual human process of interpreting and summarizing medical evidence with an automated AI-based system. The AI automatically translates narrative documents into structured computational representations, dramatically increasing processing speed and efficiency while eliminating the bottleneck of human interpretation for routine evidence processing.
Data Source
AI summary
A computational evidence platform extracts clinical concepts from medical evidence sources and creates a database of elemental diagnostic factors and elemental investigations links to medical conditions. Input from a person groups factors and investigations makes corrections and adds a ranking. Elemental factors and investigations do not include information specific to their associated conditions but include synonyms and a link to a medical ontology. A patient state is determined by extracting patient known diagnostic factors and investigation results from the patient chart. These known factors and results are matched to the database and a ranking of likely conditions are output. Next-best actions per condition are output by determining factors not yet known and investigations not yet performed. Next-best actions across conditions are determined by performing a recursive tree search of the database and assuming that unknown factors are now known to generate a score for each assumption.