The life sciences industry today is undergoing a wave of disruption, with biopharmaceutical companies embracing potential new ways to deliver a value-based model over volume by developing specialty products in target segments.
This wave of change is due to the fact that science and business operations are becoming increasingly complex, competitive and global — with companies attempting to make a comprehensive impact in this highly competitive marketplace. With increasingly complex development processes and soaring research and development (R&D) investments, stakeholders (business, end users, investigators, etc.) are looking for a more robust, reliable and reproducible approach to bringing personalized products to the market.
Even though the digital technology exists today to optimize the entire R&D value chain, adoption is low — especially in clinical trials segment — because of factors like complexity, resource-intensiveness, changing regulatory dynamics and lengthy implementations. However, while these factors may seem like an argument against implementation, the rewards are worth the effort.
As industry leaders move towards adopting personalized medicine and a patient-centric approach, it is increasingly important for clinical development enterprises to gain access to the growing volumes of patient historical data, real-world evidence, genomic profiles and emerging research to meet sponsor expectations. Harnessing this data can help investigators get a 360º view of patient performance and demonstrate the true value of new treatments to key stakeholders for effective market access.
Digital technologies can transform how companies approach clinical trial management, by enabling them to access a wealth of information from different data sources, improve patient enrollment and trial experience, capture real-time data insights and improve the quality of data collected during trials. Collectively, this can help achieve the following clinical objectives:
Expedite patient enrollment and retention by mining unstructured patient health data and identifying the right patient-trial match
Enhance patient trial experience to transform subject onboarding, trial understanding and helping set realistic expectations
Build an integrated trial management platform that can capture, integrate and analyze complex data during trials
Manage / regulate multiple sites and trace performance using advanced analytics and visualizations, enabling early intervention or shutdown of non-performing sites
Effectively manage stakeholder expectations and performance delivery
In our opinion, adopting digital technologies is an imperative strategy for clinical development enterprises. Emerging technologies like artificial intelligence, machine learning and advanced analytics are empowering CROs to reduce cost, integrate trial management, establish a tighter control, enable smart manual intervention, deliver quality outcomes and reproduce results in multiple scenarios.
In upcoming posts, we will further explore this promising synergy between technology and science to advance innovation in medicine. Stay tuned!