Background
Clinical trial success hinges on one critical factor that often determines whether a study meets its timeline and recruitment goals: site selection. For medical device sponsors, choosing the right clinical sites can make the difference between a successful trial that brings life-saving innovations to market quickly and a delayed study that struggles with patient enrollment.
Our client, a leading MedTech CRO, has been helping medical device sponsors improve healthcare since 1967. With their global clinical, testing, and regulatory expertise, they consistently deliver significant time savings compared to industry averages. However, even industry leaders face the universal challenge of optimizing site selection in an increasingly complex healthcare landscape.
The intersection of data and artificial intelligence has created unprecedented opportunities to improve this vital component of clinical research, transforming how sponsors identify and evaluate potential clinical sites.
The Challenge
Traditional site selection processes present significant obstacles that impact trial timelines and success rates:
Manual, Time-Intensive Analysis: The current approach requires extensive manual analysis of multiple disparate information sources, including Census data, ClinicalTrials.gov records, and CMS databases. This labor-intensive process consumes valuable time that could be better spent on patient care and trial execution.
Error-Prone Decision Making: Manual data compilation and analysis introduce human error and inconsistencies. Critical insights can be missed, and important patterns in the data may go unnoticed, leading to suboptimal site selections.
Institutional Knowledge Dependency: Organizations often rely heavily on individual expertise and institutional knowledge for site selection decisions. This creates vulnerability when experienced team members change roles, potentially causing valuable insights to be lost forever.
Limited Scope and Bias: Personal experience and institutional knowledge, while valuable, can be inherently limited and biased. This narrow perspective may cause sponsors to miss qualified sites that could accelerate patient recruitment and improve trial outcomes.
Fragmented Data Sources: Critical information exists across multiple disconnected sources – Medicare claims data, demographic information, and historical trial records – making comprehensive analysis difficult and time-consuming.
The Solution: Project Supreme
NyquistAI’s Project Supreme addresses these challenges through an intelligent, data-driven approach to site selection that leverages three key data sources:
Medicare Outpatient Claims Data: Licensed CMS data provides detailed patient diagnostic information linked to National Provider Identifiers (NPIs), offering direct insights into the types of patients and diagnostic procedures available at each clinical site.
U.S. Census Data: Population, age, and diversity statistics at county and zip code levels help evaluate whether potential sites can meet diversity requirements and provide cost estimates for different locations.
Historical Clinical Trial Records: NyquistAI’s database delivers comprehensive historical trial records for each potential site, enabling evaluation based on past trial experience and performance.
Advanced Technology Integration
Project Supreme connects these data sources through sophisticated natural language processing (NLP) techniques and proprietary data cleaning methods. The system links all information through clinical sites’ NPIs, creating a comprehensive view of each potential location.
The solution features an intuitive dashboard specifically designed for our client’s clinical operations team with three core capabilities:
- Intelligent Claims Data Utilization: Enables more effective analysis of CMS and EDC system data, showing patient conditions, diagnostic tests, and treatments at each site to directly assess recruitment potential.
- Comprehensive Census Analysis: Provides intuitive analysis of relevant demographic and economic data, helping teams quickly understand population characteristics and evaluate diversity requirements.
- Historical Trial Intelligence: Leverages advanced NLP techniques to match potential sites with comprehensive historical records, giving clinical teams complete visibility into each site’s trial experience.
The Benefits: Transforming Clinical Operations
Operational Excellence
- Faster Deliverables: Automated processes significantly reduce the time required for site selection, enabling quicker study startup and faster patient recruitment initiation.
- Standardized Processes: Consistent, data-driven methodology eliminates variability in site selection approaches across different trials and team members.
- Cost Optimization: More efficient site selection process reduces operational costs and enables better resource allocation.
Enhanced Decision Quality
- Bias Elimination: Data-driven decisions remove human bias and “lucky guesses,” replacing subjective assessments with objective analysis.
- Comprehensive Analysis: Access to integrated data sources ensures no qualified sites are overlooked, expanding the pool of potential locations.
- Evidence-Based Confidence: Present recommendations to clients backed by comprehensive data analysis rather than institutional knowledge alone.
Strategic Advantages
- Improved Client Service: Deliver higher quality service with more comprehensive data analysis and fact-based recommendations.
- Competitive Differentiation: Advanced AI/ML capabilities position our client ahead of competitors still relying on manual processes.
- Accelerated Innovation: Better site selection leads to faster patient recruitment, accelerating the path from concept to market for life-saving medical devices.
Long-Term Impact
- Knowledge Preservation: Systematic data analysis and storage ensure insights are retained regardless of staff changes.
- Scalable Growth: Automated processes support business growth without proportional increases in manual labor requirements.
- Patient Impact: More effective site selection ultimately means faster access to innovative medical devices for patients who need them.
Conclusion
Project Supreme represents a fundamental shift from reactive, manual site selection to proactive, intelligence-driven decision making. By harnessing the power of integrated data sources and advanced AI techniques, our client can continue delivering the time savings and quality outcomes that have made them the world’s leading MedTech CRO.
In an industry where time to market can mean the difference between life and death for patients, optimizing clinical trial site selection isn’t just about operational efficiency—it’s about fulfilling the mission to improve healthcare and save lives through faster access to innovative medical technologies.