Clinical Trial Site And Country Selection Under Enrollment Constraints
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Solution Overview
Problem
Conventional methods for selecting clinical trial sites and countries are inefficient, subjective, and prone to errors due to reliance on manual processes, limited data, and unsophisticated tools, leading to suboptimal site choices, delays, and compliance issues.
Innovation Solution
A Mixed Integer Non-Linear Program (MINLP) is used to formulate a site distribution model that minimizes the number of sites and countries while satisfying operational requirements, using advanced software tools for data integration and optimization, enabling rapid scenario generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual processes and spreadsheets are used for site selection, then flexibility and ease of operation are maintained, but productivity and accuracy deteriorate due to time-consuming iterations and human error
Solution Approach 1:
The patent replaces manual mechanical processes (spreadsheet manipulation, manual data entry, and iterative calculations) with an automated computer-based system that performs optimization algorithms and statistical analyses. This substitution dramatically increases productivity by eliminating repetitive manual tasks while maintaining ease of operation through user-friendly interfaces.
Solution Approach 2:
The system enables self-service by automatically performing data integration, analysis, and site selection recommendations without requiring manual intervention for each iteration. The automated optimization engine independently evaluates multiple scenarios and generates results, freeing users from time-consuming manual processes.
2Reliability
If comprehensive data integration and advanced analysis are implemented, then decision-making quality improves, but device complexity and computational requirements increase
Solution Approach 1:
The patent segments the complex data integration and analysis process into distinct modular components: data collection modules, data cleaning modules, statistical analysis modules, and optimization modules. Each module handles specific tasks independently, making the overall complex system manageable and maintainable while delivering comprehensive analytical results.
Solution Approach 2:
The system introduces an intermediary layer of automated data processing and analysis tools that bridge raw data and decision-making. This intermediary layer includes standardized data integration protocols and pre-configured analytical models that simplify the complexity for end users while enabling sophisticated analysis.
3Reliability
If the site selection process is expanded to evaluate more sites and countries, then the comprehensiveness of the model improves, but the time required to complete the process increases
Solution Approach 1:
The patent implements continuous automated processing that evaluates multiple sites and countries simultaneously through parallel computational operations. Instead of sequential manual evaluation, the system continuously processes large datasets and generates comprehensive results in a single integrated workflow, dramatically reducing the time required while maintaining thoroughness.
Solution Approach 2:
The system performs excessive action by evaluating far more sites and countries than traditionally considered, using automated optimization to identify the optimal subset. This approach ensures comprehensive coverage of all potential options while the algorithm efficiently narrows down to the best selections, preventing time loss despite the expanded scope.
4Adaptability or versatility
If real-time data updates and dynamic modeling are implemented, then adaptability improves, but computational energy consumption and system complexity increase
Solution Approach 1:
The system implements periodic action by updating the optimization model at strategically determined intervals rather than continuously. This approach maintains adaptability to changing conditions while significantly reducing computational energy consumption compared to real-time continuous updates. The system refreshes data and re-runs optimizations at key decision points in the clinical trial planning process.
Data Source
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AI summary
A site distribution model identifies a set of sites to satisfy operational requirements of a clinical trial. An objective function is defined as: a first element indicating whether a site is included in the clinical trial; and a second element indicating whether a country is included in the clinical trial. There is a set of constraints including that an estimated total enrollment reaches a defined target enrollment. Computer code is generated to implement a site distribution model, using an optimization modeling language, based on the objective function and the primary set of constraints. The site distribution model is solved, if possible, to produce values of the site decision variable and the country decision variable. Otherwise, it is indicated to a user that a solution is not possible. If solving the site distribution model is possible, a list of clinical trial sites is produced from the site decision variable.