Virtual patient cohorts: accelerating successful clinical development

Clinical trials generate vast quantities of data. Reusing these data assets presents enormous potential to enhance clinical development and minimizes the need to recreate datasets in new studies. However, extracting value from these resources is far from straightforward. Enter virtual patient cohorts, a transformative approach that enables faster, safer, and more cost-effective clinical development while avoiding redundant efforts.

What are virtual patient cohorts? 

Virtual patient cohorts are groups of simulated or aggregated patients derived from real-world and clinical trial data. These cohorts are created using advanced computational models and data integration techniques, enabling researchers to analyze a patient cohort without requiring direct patient involvement. By leveraging historical and existing datasets, virtual cohorts allow for the testing of hypotheses, prediction of outcomes, and refinement of study designs, all while minimizing ethical and logistical challenges. 

Virtual patient cohorts for financial and operational advantages 

Virtual patient cohorts allow researchers to create patient profiles that mimic real-world patients before entering the study phase. In this way, researchers can explore "what-if" scenarios enabling them to modify variables to observe potential outcomes. Researchers can explore specific conditions, demographic groups, or genetic markers, testing hypotheses, predicting outcomes, and refining study designs with precision. 

Exploring possible scenarios before they actually take place during a study reduces financial risk. Researchers can prepare accordingly for the study, tailoring design and optimizing efficiencies for cost-savings. 

Despite these advantages, managing the data needed to build a virtual patient cohort remains a challenge. This data is stored across diverse formats and repositories, often requiring extensive effort from dedicated data science teams to integrate and analyze effectively. 

Challenges and considerations for virtual patient cohorts 

While utilizing virtual patient cohorts offer immense potential for clinical development, they are not without challenges: 

• Data quality and integration: Combining data from multiple sources requires standardization and validation to ensure accuracy. 

• Privacy and security: Safeguarding patient anonymity and compliance with data protection regulations (e.g., GDPR, HIPAA) is paramount. 

• Interpretation limitations: Virtual cohorts rely on historical data, which may not fully represent emerging trends or unforeseen variables.  

Knowledge graphs for virtual patient cohorts 

A knowledge graph can be an invaluable tool in building a virtual patient cohort by organizing and integrating vast amounts of medical data from various sources, such as electronic health records, clinical trials, and more. It connects different pieces of data—such as patient demographics, disease history, treatment responses, and comorbidities—into a structured network, allowing for more precise identification of relevant cohorts. With a knowledge graph, researchers can efficiently query and visualize patient data to create highly specific virtual cohorts that mimic real-world patient populations. 

How DISQOVER optimizes virtual patient cohort building 

DISQOVER is ONTOFORCE’s intuitive knowledge discovery platform built on knowledge graph and semantic technology. With DISQOVER’s powerful data integration capabilities, clinical research teams can quickly analyze the data needed to assemble a virtual patient cohorts.

Rapid data Integration for streamlined cohort creation  

DISQOVER’s data ingestion engine and underlying knowledge graph enable users to rapidly link and integrate all relevant data sources from across clinical trials. Its adaptable data model accommodates new data as it becomes available, ensuring researchers can quickly build comprehensive datasets. 

Effortless visualization and refinement of criteria  

With DISQOVER, researchers can easily explore datasets spanning multiple trials, data sources, and concepts. The platform’s user-friendly interface allows users to visualize, filter, and refine criteria seamlessly, enabling them to select the most relevant data points for their analysis. 

Confidence through data provenance  

Fine-grained data provenance at the property level provides researchers with confidence in the origin and reliability of their data. This transparency ensures trust in the insights derived from the platform. 

Empowering researchers beyond data science expertise  

Unlike traditional data systems that require extensive support from data scientists, DISQOVER’s intuitive user interface is designed to be accessible to clinical researchers, regardless of their technical background. This democratizes access to advanced data analysis, enabling researchers to explore, query, and visualize data independently.  

Revolutionizing clinical development with DISQOVER 

By leveraging DISQOVER’s capabilities for building virtual patient cohorts, researchers can navigate the complexities of clinical trial data with unprecedented efficiency and confidence. This not only accelerates the pace of clinical development but also enhances the safety and reliability of patient outcomes.  

Learn more about what's possible for virtual cohort building in DISQOVER