TCS ADD™ Risk-Based Quality Management
Transform your clinical trials with our AI-driven RBQM platform. Enhance efficiency, ensure compliance and drive quality.
TCS ADD™ Risk-Based Quality Management (RBQM) is an end to end integrated, AI- driven platform modelled on FDA and ICH guidelines on ‘Quality by Design’ and managing ‘Critical to Quality’ factors for transforming clinical trials by implementing comprehensive risk assessment, monitoring, issue identification and mitigation across operational and clinical data.
Our RBQM platform significantly accelerates time to market by improving data quality, study/site oversight, reducing workload by predictive analytics, enhancing patient safety and regulatory compliance.
Clinical trial monitoring is facing challenges due to the increased complexity of trials and an increasing number of data sources.
This has resulted in operational inefficiency and difficulties in signal detection. Siloed systems, time-consuming study onboarding activities, lack of required skill sets, and limited AI/ML capabilities are hampering conducting clinical trials efficiently, impacting costs and quality adversely. Manual review processes, absence of end-to-end workflows, and difficulties in bringing in cross-functional collaboration are delaying the adoption of risk-based monitoring strategies.
The TCS ADD™ Risk-Based Quality Management platform accelerates new drug/therapy development timelines.
The platform provides the products and technology to enhance risk assessment, monitoring, quality control, compliance, site performance, and clinical and operational data analytics capabilities. Our unique platform approach allows flexibility, enabling organizations to adapt the tools that best meet their immediate needs while gradually integrating a comprehensive risk-based quality management (RBQM) solution.
TCS ADD™ Risk-Based Quality Management platform is powered by four cutting-edge components encompassing end-to-end risk assessment, statistical monitoring, and mitigation of risk.
Real-time risk assessment, monitoring and mitigation
Scalable, modular, and flexible
Encompasses over 40 out-of-the-box KRIs and critical data points and site/patient risk scores
System generated alerts and end to end user driven workflows
Enables efficient study onboarding in a near-touchless manner
Extensive KRI/KPI/ QTL/CDP library for user-driven study configuration
The Risk Assessment and Categorization Tool (RACT) is a highly intuitive component that aligns with the industry–standard approach for risk identification and mitigation of critical data and critical processes of clinical trials. The platform facilitates early identification and planning for potential risks that could negatively impact the safety, efficacy, and integrity of the study and study data.
Key features:
Clinical Trial Analytics leverages data-driven insights to optimize the planning, execution, and oversight of clinical trials with a focus on improving trial efficiency, ensuring compliance, and enhancing overall trial quality through robust analytics. The offering facilitates user-driven configuration and centralizes statistical monitoring of over 40 key risk indicators such as patient recruitment rates, protocol deviations, and data entry lags, enabling timely interventions. In addition, it enables patient-specific analytics to detect anomalies in patient data and monitor patient adherence to treatment protocols, and identify patterns that may indicate non-compliance.
Key features:
The QTL module allows the study team to proactively monitor systemic issues that could negatively impact the safety and efficacy of clinical trials.
Key features:
An advanced and integrated AI/ML-based solution that enables the seamless monitoring of patients’ medical data for timely decision-making. It facilitates patient profiling with risk scores, outlier detection, and alerts and notifications to easily identify safety risks and trends.
Key features:
The TCS ADD™ Risk-Based Quality Management platform commands global usage covering several therapeutic areas.
The key benefits include:
Increased efficiency and reduced workload
Improved data quality and oversight
Meaningful insight and prevention of issues