Cognitive Clinical Trial Feasibility Analysis System
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Solution Overview
Problem
Current clinical trial site feasibility assessments are largely manual and dependent on investigators, leading to incomplete information, miscommunication with patients, and a lack of well-defined site selection criteria, making it challenging to conduct reliable and efficient clinical trials across diverse patient populations and global locations.
Innovation Solution
A data-driven cognitive clinical trial feasibility analysis method and system that receives protocol requirements, identifies meta-data, obtains exhaustive historic and third-party site data, and assesses site feasibility using Key Performance Indicators (KPIs) and Key Risk Indicators (KRIs to determine Red, Amber, and Green scores, with neural network-based estimation for inadequate data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual feasibility assessment methods are used with investigator dependency, then flexibility in handling diverse clinical trial requirements is maintained, but information completeness and reliability deteriorate due to limited experience and rough estimates
Solution Approach 1:
The patent introduces a cognitive system with NLP capabilities as an intermediary between protocol requirements and feasibility assessment. This system automatically extracts meta-data from unstructured protocol documents and queries multiple data repositories (internal and third-party) to provide comprehensive, reliable site feasibility information without relying on investigator experience
Solution Approach 2:
The patent replaces the manual mechanical process of investigator-based feasibility assessment with an automated cognitive system that uses NLP, machine learning, and data querying mechanisms to extract, analyze, and evaluate clinical trial site feasibility information from multiple sources
2Measurement precision
If exhaustive historic and third-party data is collected and analyzed using cognitive systems, then measurement precision and reliability of site feasibility assessment are improved, but device complexity and data processing requirements increase
Solution Approach 1:
The patent segments the complex feasibility assessment system into distinct functional modules: NLP processing module for extracting meta-data from protocols, data querying module for accessing internal and third-party repositories, analysis module for evaluating site feasibility, and reporting module for generating assessments. This modular architecture manages complexity while maintaining high measurement precision
Solution Approach 2:
The cognitive system is designed as a universal platform that can handle diverse protocol requirements, query multiple types of data repositories (internal and third-party), and assess various site feasibility criteria through a single integrated system, reducing overall complexity through consolidation
Data Source
AI summary
This disclosure relates generally to clinical trial management, and more particularly to method of performing a data driven cognitive clinical trial feasibility analysis. In one embodiment, the method comprising (a) receiving, a plurality of protocol requirements to initiate a clinical trial site feasibility; (b) identifying, a plurality of meta-data for at least one protocol requirement from the plurality of protocol requirements; (c) obtaining, an exhaustive list of historic clinical trial site data for the identified meta-data from a site data repository; (d) obtaining, an exhaustive list of third party clinical trial site data for identified meta-data from a third party data repository; and (e) assessing, the exhaustive list of clinical trial site data and the exhaustive list of third party clinical trial site data to obtain a list of identified clinical trial site feasibility.