AI Clinical Trial Protocol Generator for Automated Design and Validation
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Solution Overview
Problem
Current clinical trial protocol design processes are manual and labor-intensive, leading to sub-optimal implementations, data quality issues, delays, and resource inefficiencies due to the need for manual data consolidation and communication between multiple sources, which can result in inconsistencies and errors.
Innovation Solution
An AI and Machine Learning-based system that acquires raw data from disparate sources, processes it to deduce meaningful information, and uses a Trial Planning and Design module to generate a clinical trial protocol design document, incorporating pre-Drafted and regulatory protocols, with predictive validation and approval mechanisms, while also selecting investigators and patients based on correlated features and historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual extraction of information from multiple sources is used to generate protocol designs, then flexibility and adaptability are maintained, but time consumption increases and data consistency deteriorates
Solution Approach 1:
The system segments the protocol design process into distinct modules: data acquisition from multiple sources, data processing and consolidation, protocol generation, and validation. This modular approach enables automated parallel processing of different data sources while maintaining the flexibility to customize each module, thereby reducing overall time consumption without sacrificing adaptability.
Solution Approach 2:
An intermediary AI-based data consolidation engine is introduced between multiple data sources and the protocol generation process. This intermediary automatically standardizes and integrates data from diverse sources (electronic health records, research databases, regulatory documents), eliminating manual consolidation efforts while preserving the ability to adapt to different data formats and sources.
2Loss of information
If manual data consolidation from various input sources is performed, then data from archived sources can be incorporated, but data inconsistencies and errors increase
Solution Approach 1:
The system implements feedback mechanisms where the AI model continuously validates consolidated data against predefined consistency rules and quality metrics. Data from archived sources is cross-checked with current data sources, and discrepancies are automatically flagged and resolved through iterative refinement, ensuring high data consistency while maintaining completeness.
Solution Approach 2:
The system dynamically adjusts data processing parameters based on the source and type of data being consolidated. Different transformation rules, validation thresholds, and integration strategies are applied to different data sources (e.g., structured EHR data vs. unstructured research notes), enabling reliable consolidation of diverse data while maintaining consistency standards.
3Reliability
If cross communication between Subject Matter Experts is required, then domain expertise is incorporated, but communication challenges and data inconsistencies arise
Solution Approach 1:
The system enables Subject Matter Experts to contribute domain knowledge through self-service mechanisms such as annotated data validation, protocol template customization, and automated query resolution. Experts review and validate AI-generated protocol sections independently, providing domain expertise without requiring extensive inter-expert communication, thereby reducing communication complexity while maintaining expertise quality.
4Manufacturing precision
If manual protocol design tasks are performed, then detailed control over protocol elements is maintained, but productivity and efficiency decrease
Solution Approach 1:
The system performs preliminary actions by pre-processing data from multiple sources, pre-validating data quality, and pre-generating protocol sections using AI models before final assembly. This preliminary work ensures high accuracy in data consolidation and protocol elements, while the automated nature of these preliminary steps significantly increases overall productivity compared to manual processes.
Data Source
AI summary
Disclosed is a system for generating Clinical trial protocol design document with selection of a Patient and an Investigator for a clinical trial process. The system inputs meaningful information derived from the raw data, a pre-Drafted protocol, a regulatory authorities' protocol curated by regulatory authorities, and a pre-stored dataset, present in a repository. A Clinical trial protocol design document is drafted by generating a case frame upon extracting data in form of a key value into a standard document. Each key value is validated and a prediction score is computed based on overlapping of the interim Clinical trial protocol design template with the pre-Drafted protocol and the regulatory authorities' protocol to determine whether the interim Clinical trial protocol design document is approved or rejected. A Clinical trial protocol design document is generated when the interim Clinical trial protocol design document is approved.