Clinical Trial Design Platform Simulation
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Solution Overview
Problem
Clinical trial design optimization is hindered by traditional methods' inability to evaluate and compare a large number of design options, leading to suboptimal choices that result in high costs and prolonged completion times, due to the complexity of trial design requiring statistical, clinical, and software expertise that many organizations lack.
Innovation Solution
A trial design platform that utilizes cloud and distributed computing to simulate hundreds of millions of study design variants, leveraging advanced simulations, visualizations, and methodological knowledge to identify optimal designs and their performance drivers, supporting collaboration across organizations and providing insights into design tradeoffs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional methods are used for clinical trial design, then the process is simpler and requires less computational resources, but the ability to evaluate and compare design options is severely limited
Solution Approach 1:
The patent segments the clinical trial design evaluation process into discrete simulation modules that can be independently executed and combined. Each simulation evaluates specific design parameters, allowing the system to handle complex evaluations by breaking them into manageable computational units that can be processed in parallel across distributed computing resources.
Solution Approach 2:
The patent introduces a computational platform as an intermediary between clinical trial designers and evaluation results. This platform automates the complex simulations and computations, mediating between the input design parameters and the output performance metrics, thereby enabling comprehensive evaluation without requiring end users to possess specialized computational expertise.
2Productivity
If traditional methods are used for clinical trial design, then computational resources required are minimal, but the number of design options that can be evaluated is limited
Solution Approach 1:
The patent transitions from single-node computational evaluation to distributed multi-node computing architecture. By adding the spatial dimension of distributed computing nodes, the system can evaluate exponentially more design options simultaneously, transforming the computational capacity from linear to parallel processing across multiple machines.
Solution Approach 2:
The patent performs preliminary computational setup by pre-configuring simulation parameters, design spaces, and evaluation criteria before the actual trial design evaluation. This preliminary action prepares the computational framework in advance, allowing the system to rapidly evaluate numerous design options once initiated, thereby increasing productivity while managing computational resource usage efficiently.
3Measurement precision
If comprehensive simulation of multiple design options is performed, then optimal trial design can be identified, but the time and computational cost increase significantly
Solution Approach 1:
The patent implements periodic evaluation cycles where simulations are executed in structured batches or iterations rather than continuously. Design options are evaluated in periodic cycles, with results aggregated and analyzed at each stage, allowing the system to maintain high measurement precision through comprehensive evaluation while managing time loss through structured, periodic processing rather than exhaustive continuous computation.
Data Source
AI summary
A method and system including identifying, via at least one processor, a user role; configuring, via the at least one processor, an interface for the user role, wherein configuring the interface includes determining an interface view for the user role; and displaying, via the at least one processor, the interface with the interface view.