Digital Transformation: Improving Efficiency in Managing Product Life Cycles

AI and machine learning have important and growing roles in all stages of the development of transformative technologies as exploratory tools to improve productivity and operational efficiency. Examples include the identification of new molecular targets, non-clinical screening, and timely execution of clinical development—including its monitoring and supply chain optimisation. The goal behind the use of AI/ML is to expedite product development and patient access. AI can analyse vast datasets and identify what would otherwise be imperceptible to human researchers in the effort to unravel intricate biological interactions and disease manifestations. AI and ML have demonstrated their value by having had a convincing positive impact on diagnostic medicine by improving patient care. Major challenges to its general application reside in the quality of data used to predict outcomes with precision and accuracy.

The common goal for improving market access is to remove any hurdle that could prevent timely patient access and stifle innovation. Policy makers are increasingly imposing measures seeking to contain healthcare costs. Greater focus is now placed on the therapeutic value of a new treatment modality through production of evidence of meaningful outcomes, and the financial impact on healthcare budgets. The complexity of the market access environment requires a more strategic, collaborative, and adaptive approach to be adopted by integrating market access considerations throughout the product lifecycle to optimise product adoption in health systems.

There is a greater use of interconnected digital supply networks based on interoperable data to optimally manage the supply chain. Advanced analytics are deployed to forecast demand, to manage inventory, and to optimise operational logistics. The overall goal is to improve efficiency across the product life cycle. It becomes especially important when addressing the unique and emerging needs of the cell and gene therapy supply chains for manufacturers to optimise distribution and to support infrastructure under strict time and storage controls.

There is a significant shift in the clinical trial model by embracing patient-centric decentralised trials—a trend which has been accelerated by the COVID-19 pandemic. The purpose is essentially two-fold: (a) to expand the breadth of data collection and (b) to make clinical trials more convenient and accessible to potential trial participants. To this end, greater engagement with external stakeholders and contractors is necessary to ensure the alignment of the study design with the needs and preferences of trial participants and patients.