TCS ADD™ Medical Monitoring
Revolutionize analyzing the subject data in your clinical trials boost precision, efficiency, and reliability.
Industry
Life Sciences
Patient-centered Care Models Taking Center Stage in Life Sciences
Products and Platforms
TCS ADD™
TCS ADD™: A Suite of Modern and Open Technology Platforms
Highlights
- Clinical trials are beset with challenges, and life sciences companies are turning to data to help them make better decisions and enhance efficiency. But managing and understanding extensive amounts of data, both internal and external, is no mean feat.
- Data review is, hence, one of the biggest parameters for trial success, and providing quick and accurate reviews by data reviewers and medical monitors early in the trial promises success.
- TCS ADD Medical Monitoring is a comprehensive solution that enhances subject data analysis by harnessing artificial intelligence and machine learning technologies. It streamlines data collection, preparation, and analysis—enabling organizations to derive accurate, actionable insights faster.
Overview
As clinical trials get more complex than ever before, industry players and clinical research organization (CROs) face challenges such as understanding intricate datasets and reviewing voluminous records while maintaining data integrity and quality.
These challenges highlight the importance of proactive data review for trial success. Organizations that present data intuitively and enable data reviewers and medical monitors to review it quickly, seamlessly, and accurately—early in the trial rather than later, are poised to succeed.
TCS ADD Medical Monitoring solution checks all these boxes. It functions as a plug-and-play module that integrates with multiple data sources such as electronic data capture (EDCs), electronic health records (EHRs), labs, and electronic patient-reported outcome (ePROs), supporting studies across traditional pharma, cosmetics, and animal health. Its robust query management workflow allows all stakeholders to communicate effectively and maintain full visibility into trial data—whether to track review completion, identify data discrepancies, or flag high-risk subjects.
Solution
TCS ADD Medical Monitoring solution redefines patient safety through intelligent medical monitoring.
Core features of the solution are mentioned below:
Clinical data review
Facilitates a comprehensive and highly intuitive review of the clinical data, including those related to biomarkers and labs. Powered by AI/ML-driven insights, it includes anomaly detection and patient profile scoring.
Multipoint ingestion
Consolidates data from over 15 sources to ensure a holistic view of clinical trial data, thereby enabling more informed decision-making.
Critical data point library
Configurable CDP and metrics library for subject data with the study-specific configuration of ‘priority alerts’ and ‘data of interest.’
Interactive analytics
Interactive study, site, and subject-level data trends across more than 50 clinical metrics.
System and user-generated actions
Unified and collated view of an inbuilt system generating actions for pending clinical data review and collaboration activities.
Statistical analysis
Driven by protocol-specific configurations, one-touch statistical analysis, and report generation from complex trial data, it depicts changes in efficacy and safety of an investigational product over time, providing a comparative view between multiple products.
Benefits
Driving smarter, safer clinical trials.
Timely decision-making
- Our solution supports medical monitors and data scientists in swift, informed decision-making by continuously analyzing patient data and providing actionable insights.
- Achieve up to 30% faster data reconciliation and analysis by swiftly identifying and resolving potential issues—accelerating decision making and trial timelines.
Enhanced patient safety
- Early detection of anomalies and potential risks allows proactive measures, reducing the likelihood of adverse events.
- Reduce adverse events (AEs) by up to 10% with proactive safety monitoring enabled by patient and site risk prediction.
Trend identification
- Our system's advanced pattern recognition capabilities enable the identification of trends within patient data, facilitating the prediction of patient outcomes in clinical trials and the implementation of proactive measures.
- Predictive capabilities streamline clinical trial processes, potentially reducing time and costs associated with drug development.
Improved data quality and oversight
- Up to 20% increase in data quality and integrity with the detection of duplication and missing data collection, and early identification of trends and signals.
- End-to-end integrated risk identification, monitoring, and mitigation workflows.
- Meaningful insight and prevention of issues.
- Automated and intelligent data intake from over 15 diverse data sources resulting in novel insights.
TCS advantage
TCS empowers life sciences organizations with a future-ready platform built for scalability, flexibility, integration, and delivering end-to-end value across clinical operations.
- Scalability: Intelligent seamless pipeline catering to data processing, scaling to handle diverse sources, study volumes, and metrics at scale with optimized resource pooling.
- Flexibility: Companies can implement the highly modular platform as a standalone solution on top of the existing solutions or integrate with other TCS solutions for life sciences.
- Cross-solution integration: The platform is modular and interoperable, seamlessly integrating with upstream and downstream systems, and horizontally with other TCS ADD offerings.
- End-to-end offering: TCS provides IP-led solutions combined with clinical service capabilities, helping life sciences organizations leverage a one-stop destination for serving their business requirements.
- Technology and domain expertise: TCS-certified subject matter experts combine the capabilities of clinical research service providers, platform solution providers, and global systems integrators to deliver strategic solutions fulfilling customer needs.
