Solution
Cloud
Enabling Enterprise Agility & Resilience with a Robust Cloud Strategy
Consulting
TCS Consulting Services to Achieve Success in the Business 4.0 Era
TCS ADD™
TCS ADD™: A Suite of Modern and Open Technology Platforms
Industry
Healthcare
Patient-centered Care: Health and Wellness Redefined
Life Sciences
Patient-centered Care Models Taking Center Stage in Life Sciences
Highlights
- Companies are increasingly employing integrated technology solutions in clinical development to enhance efficiency, speed, and value.
- A unified solution—one that encompasses real-time data, advanced technologies, and domain knowledge—makes it convenient for patients participating in clinical trials. It allows for preventive interventions such as fit-for-purpose, risk-based monitoring for trial sites and patients.
- A unified technology solution can address clinical development life cycle challenges and improve efficiency by about 40% to 50%.
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A unified domain-led technology solution
An integrated technology solution is vital to achieving regulatory compliance.
New technologies with a transformative impact drive the shift in approach—from application-centric to data-centric. A unified domain-led technology solution can solve the challenges associated with disparate systems, data redundancy, and lack of collaboration across data and documents. It unlocks opportunities to improve efficiency, speed, and value, which drive business outcomes, including real-time data collection. This can reduce the overall cycle time by around 25% - 40%, accelerating the end-to-end clinical development process.
The solution will empower patient volunteers and investigators, enabling digital data initiatives, cognitive capabilities, and sensor-based intelligent systems. Additional key outcomes envisaged from the unified technology solutions are outlined below.
Data-driven insights for better study planning and start up
Adopt standards to digitize study metadata for downstream acceleration.
Deriving insights from historical, medical, and scientific data for an optimized protocol design can be achieved using advanced analytics.
A data-driven approach to assessing the feasibility of the clinical trial site will help build a robust strategy by selecting the best-performing facilities per phase, therapeutic area, indication, country, treatment, eligibility criteria, and age group.
Modern data acquisition strategies advocate standardization, reuse, and the availability of a single source of truth for optimal data flow and governance. With patient centricity at the core, decentralized clinical trials with electronic consent (eConsent), electronic clinical outcome assessment (eCOA), and electronic patient-reported outcome (ePRO) are expected to add value to the patient journey.
One must view the overall risk in the ever-changing and dynamic regulatory landscape to strategize effectively. A unified technology-led solution must address these risks and bring in interoperability for faster analysis and insights to realize the value of intelligence.
Operational excellence in trial management
Better integration and seamless data flow will enable data reuse, driving continuous improvement.
Further integration with the pharma data ecosystem using FAIR principles, statistical analysis, advanced analytics using AI, ML, and NLP, and visualization will gain more operational, scientific, and medical insights from the collected data.
A robust patient engagement strategy entails visit reminders, and medication adherence, encourages involvement, and helps avoid compliance issues throughout the trial lifecycle.
The integrating technology solution makes the trials convenient for the patient. It enables real-time tracking of issues and risks, improving the efficiency of the clinical trial site with robust crisis management and increased decision-making ability. It allows for preventive interventions such as fit-for-purpose, risk-based monitoring for sites and patients.
Trial teams can focus on core data engineering activities, working with complex data knowledge with next-gen technology such as AI and ML. Integrating the data ecosystem will enable near real-time automated data transformation and scientific analysis. This will foster early decision-making and lead to innovative treatments reaching patients faster.
A structured authoring process powered by AI and NLP technologies and a robust TMF solution can transform submissions. Further standardization in master data management improves the supervision of product registrations.
Optimize study close-out, reporting, and archival for timely submission
The unified technology-led solution will effectively address the challenges of data inconsistency.
These include data complexity in format, structure, and content. Regulatory authority requires a validated publishing output, such as the electronic common technical document (eCTD) structure, non-eCTD electronic submission (NeeS), or paper submission, to ensure compliance. The solution should enable an iterative process with real-time tracking at every transaction or milestone and monitor submission progress through the electronic gateway.
The unified technology approach ensures assimilation between clinical, regulatory, and quality functions with a common entry point, process standardization, traceability, and business intelligence. A unified and ubiquitous approach to human-computer interaction results in higher user adoption rates, lower training requirements, and increased productivity.
Address the current gaps
The integrated solution can identify gaps and improve efficiency across the clinical development life cycle.
The current gaps across the clinical study lifecycle are:
• Inefficient study design due to lack of historical insights embedding regulatory intelligence
• Manual patient, site, and investigator engagement methods and inefficient document management
• Inept critical milestone tracking and interoperability due to duplicate data and documents leading to delays in patient enrolment and an increase in protocol amendments
• Inefficient operational process impacting timeline from last patient last visit (LPLV) to final tables, listings and figures (TFLs), database lock (DBL) to clinical study report (CSR)
• Disparate systems for data compilation and archival lead to limited process performance and disconnected data from collection to analysis
Enabling business outcomes
Enabling business outcomes
These gaps can be addressed by:
• Optimizing the study design with an intelligent protocol enabled by AI-ML and leveraging insights from historical data and regulatory intelligence for change management.
• Improving patient and site engagement with predictive analytics, site forecasting, and digital enablement (DCT)
• Enabling predictive patient density mapping by AI- and ML-led data cleaning methods to improve the patient site and investigator experience
• Enabling metadata-driven operational data for AI-led clinical trial analytics and ensuring real-time and ready availability of knowledge through digital interventions
A unified solution enabled by domain and technology-led interventions helps break silos by applying a comprehensive, end-to-end process and cross-functional analytical approach. Adopting data-driven strategies, AI in the workflow, and a digital culture enables the acceleration of the clinical development process, improving the speed and delivery of treatments for patients.
