JANUARY 2023LIFE SCIENCES REVIEW9data quality and accessibility, and international collaborations. For regulatory purposes, in particular, early engagement with regulators will support subsequent efforts to obtain and analyze observational data. Finally, in an era of digital innovation, AI may enable extensive collection, aggregation, analyses, and interpretations to generate evidence and insights. Furthermore, regarding the definition and applications of AI, the purpose of AI for drugs versus device regulations, the availability of quality data fed into explainable algorithms, as well as ethical conduct must be carefully considered. Barriers When Applying AIPotential bottlenecks exist for RWE generation for value-added purposes, e.g., supporting regulatory submissions, label expansions, value-based contracts, comparative effectiveness research, etc. For example, claims data are typically generated for insurance billing purposes, not adjudicated in terms of data quality, and medical errors can exist. Refinement, or training, of analytic algorithms is essential to improving the accuracy, quality and speed of RWD analytics. Researchers must first ensure that the data obtained are complete and relevant to the condition, patient population, and treatment analyzed. Unstructured data such as texts may contain relevant information for only certain sub-populations or information may be entered for some patients but not others. Even structured data pose challenges in the application of RWD analytics and AI since data may have inconsistent terms, different formats between sources, and have incomplete or messy information. These situations might lead to inaccuracy in the analyses and convergence of the algorithms.In parts of the world, data aggregation may pose an equally significant challenge. Legal barriers around data privacy, practical barriers related to data storage across multiple organizations, and economic barriers involving lack of incentives for organizations to collaborate and share data, all affect the availability of data to which analytic tools and algorithms can be applied. Additional challenges are likely to be encountered in this fast-moving field. It is also worth emphasizing a well-known saying, "garbage, in garbage out." Data standards not only cover the quality aspect but also common data model applications. It is also critical to identify multi-disciplinary partnerships and talents who can be skillful in harnessing big data. Future of AI-Driven RWE AI can be useful in a variety of ways, e.g., process automation, medicines regulation, monitoring medication adherence, digital innovation via eHealth, mHealth and telehealth, and sophisticated algorithms. [5] Besides usefulness in harnessing RWE, AI can play a critical role in optimizing RCTs and generating evidence through pragmatic clinical trials (PCTs). If challenges and barriers can be overcome successfully, with large-volume data shown to provide sufficiently accurate and comprehensive evidence-generation, AI has the potential to shorten the timeline for clinical trial design and regulatory approval, and to uncover patterns in large sets of data that would otherwise not be observed. Finally, while the use of AI to capture, amalgamate, standardize, and analyze RWD is still evolving, it has a potential to support the increased availability of data to improve global health and healthcare now and well into the future. Researchers must first ensure that the data obtained are complete and relevant to the condition, patient population, and treatment analyzed
< Page 8 | Page 10 >