Clinical Trial Site Identification via Data Standardization
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Solution Overview
Problem
Pharmaceutical companies face challenges in quickly identifying locations with large patient pools for clinical trials, leading to increased costs and competitive disadvantages due to the lack of effective tools for streamlining clinical trial sites based on geographical needs.
Innovation Solution
A method and system that combines clinical trial data with socioeconomic, demographic, and epidemiological data, using geo-spatial visualization tools to correct address errors and standardize data, generating an initial list of clinical trial sites and investigators, thereby identifying locations with significant patient pools and minimal competitor activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If pharmaceutical companies manually identify clinical trial sites without specialized tools, then they can conduct clinical trials, but the process becomes lengthy and costly
Solution Approach 1:
The patent introduces a specialized software tool as an intermediary between pharmaceutical companies and clinical trial site identification. This tool automatically collects, processes, and analyzes data from multiple sources (epidemiological databases, clinical trial registries, healthcare provider directories) to generate optimized site recommendations, eliminating the need for manual research and significantly reducing identification time
Solution Approach 2:
The system performs preliminary actions by pre-collecting and pre-processing clinical trial data, patient pool information, and site characteristics before they are needed. The tool maintains updated databases of potential trial sites with pre-analyzed patient demographics and disease prevalence data, allowing companies to quickly identify suitable locations without starting from scratch
2Quantity of substance
If pharmaceutical companies increase the size and complexity of clinical trials to enroll tens of thousands of patients, then they can achieve more comprehensive trial results, but they need better tools to identify patient pools
Solution Approach 1:
The patent segments the complex task of identifying patient pools into manageable components: (1) defining inclusion/exclusion criteria, (2) querying epidemiological databases for disease prevalence, (3) identifying healthcare providers in those regions, (4) estimating patient availability at each site. This segmentation transforms an overwhelming complex problem into a systematic multi-step process that can be automated
Solution Approach 2:
The software tool performs multiple functions within a single integrated system: it acts as a data collection platform, analysis engine, site recommendation system, and project management tool. This multi-functionality reduces the complexity that would otherwise require multiple separate tools and processes to handle different aspects of patient pool identification
3Productivity
If pharmaceutical companies want to accelerate clinical trials to reduce costs and gain competitive advantage, then they need to quickly identify locations with large patient pools, but they lack effective tools for streamlining clinical trial sites
Solution Approach 1:
The system enables self-service by allowing pharmaceutical companies to independently conduct their own site identification without relying on external consultants or manual processes. The tool provides automated data collection, analysis, and recommendation generation that companies can access and use directly, making the process easier and more accessible
Data Source
AI summary
A method and system for identifying clinical trial sites is provided. The method includes receiving clinical trial data associated with a plurality of planned clinical trials. Portions of the clinical trial data are identified based on differing data sources. Relevant information is extracted from the portions. Socioeconomic data, demographics data, and epidemiological data are received and combined into a common format. Incorrect address data is corrected and the clinical trial data, socioeconomic data, demographics data, and epidemiological data are standardized. In response, an initial list is generated. The initial list includes associated principle investigators and clinical trial sites associated with planned clinical trials overlaid on the clinical trial data, the socioeconomic data, the demographics data, and the epidemiological data.