Clinical Data Science

Clinical Data Science is a module within the ThoughtSphere platform designed for clinical data exploration, modeling, and analysis. It is intended for data scientists, clinical researchers, and developers working with clinical trial data, real-world evidence (RWE), and historical datasets to identify patterns, detect anomalies, set benchmarks, and predict study outcomes.

The module supports translational medicine use cases by combining real-time curated data with imported historical and RWE datasets, and provides tooling for both pre-built and custom AI/ML model development across leading programming languages.

Real-Time Modeling and Data Exploration

• Develop models using real-time, curated data as well as imported historical and RWE datasets to support translational medicine.
• Create dynamic AI/ML models to predict outcomes and find patterns in complex, unstructured data.
• Supports R, Python, and SAS programming languages.

Library of Pre-Built Models

• Includes a configurable library of pre-built models for surfacing correlation trends, digit preferences, and duplicate patient records.
• Each model includes drill-down capabilities to support root-cause analysis.

Data Science Collaboration Capabilities

• Automated process workflows and notebook capabilities support data governance and data sharing.
• Enables developers across multiple locations to collaboratively author, revise, validate, and execute data models in real time.

Clinical Data Science is part of the broader ThoughtSphere platform, which also covers study oversight and analytics, risk-based quality management, data quality management, medical monitoring, biostatistics and programming, data curation and archival, site payments and contracting, and safety case transformation.