Clinical Protocol Document Generation With Indexed LLM Validation
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Solution Overview
Problem
The generation of clinical trial documents and data using large language models is time-consuming and costly due to the need for extensive manual effort, and there is a risk of inaccuracies from hallucinations arising from learning on general public data.
Innovation Solution
A system and method utilizing a large language model to generate documents and data based on clinical protocols, incorporating a service provision module with conversion rule management and a clinical protocol index unit, along with an assistant learning module to enhance accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual generation of clinical trial documents and data is used, then accuracy can be maintained, but time consumption and cost increase significantly
Solution Approach 1:
The patent introduces a large language model as an intermediary between the clinical protocol and the generated documents/data. The LLM processes the clinical protocol input and generates the required documents and data, acting as a mediator that automates the generation process while maintaining accuracy through structured prompt engineering and conversion rules.
Solution Approach 2:
The system performs preliminary actions by pre-defining conversion rules, prompts, and document templates before the actual document generation process. The clinical protocol is parsed and structured in advance, and the LLM is prepared with predefined instructions and templates, enabling efficient and accurate document generation without manual intervention during the actual production phase.
2Productivity
If large language model is used to generate clinical trial documents, then time and cost are reduced, but hallucination and inaccuracies occur
Solution Approach 1:
The patent implements feedback mechanisms where the generated documents and data are validated against the original clinical protocol and predefined conversion rules. The system checks whether the LLM output conforms to the expected format, structure, and content requirements, providing feedback loops that correct hallucinations and inaccuracies while maintaining high generation efficiency.
Solution Approach 2:
The system replaces manual mechanical verification processes with automated validation mechanisms. Instead of manual review of each generated document, the patent employs automated checking systems that verify the LLM output against the clinical protocol and conversion rules, efficiently detecting and correcting hallucinations without sacrificing accuracy.
3Quantity of substance
If comprehensive documents and data are generated for clinical trial, then completeness is improved, but the complexity of the generation process increases
Solution Approach 1:
The patent segments the document generation process into distinct modular components: clinical protocol parsing, conversion rule application, LLM prompt generation, document template filling, and validation. Each component handles a specific aspect of the generation process, making the overall complex system manageable and maintainable while ensuring comprehensive document generation through systematic processing of all required elements.
Data Source
AI summary
A system for generating related documents and data on the basis of a clinical protocol using a large language model includes: a service provision module for receiving a clinical protocol from at least one user terminal, generating documents and/or data related to the clinical protocol, and providing the documents and/or data to the at least one user terminal; and a large language model for receiving at least one prompt and information related to the clinical protocol indexed by the service provision module from the service provision module, and providing an output thereof to the service provision module, wherein the service provision module includes: a conversion rule management unit for providing a conversion rule generation function including the at least one prompt to the user terminal; and a conversion execution unit for providing the conversion rule and the clinical protocol to the large language model, and providing documents and/or data output from the large language model to the at least one user terminal in a final output form.