Advances in technology are now keeping pace with an increase in available data. These advancements provide an unparalleled opportunity to bring together the most meaningful RWD, from claims data to physician notes to wearable device data and beyond. By first standardizing and then linking the collected data, one is able to create RWE capable of addressing specific questions asked at the beginning of a research study or asked by regulatory and market access stakeholders.
Natural language processing (NLP) is used to “read” unstructured RWD and machine learning to expedite advanced analytics and predictions within a data platform. Combined with powerful data handling, they drive efficiency and access to immediate insights. With these advanced technology tools in place, the ongoing ingestion, processing and analysis of new data can propel biopharma companies to improved, data-driven and evidence-based decision-making throughout the product lifecycle.
Creating a strategy to drive this process and identifying which data sources will provide the richest analytic landscape are also important steps to consider. It has become increasingly important for companies to utilize a broad array of RWD sets to measure a product’s true performance through real-world outcomes. This can then reinforce the product’s inherent value in the marketplace. Having access to a powerful, agile technology platform will ensure that no matter what data sources become available for future analysis, the framework is in place to support evolving requirements.
Join Bill Row, Divisional Principal, Real World Evidence Strategy and Analytics, ICON plc, Bruce Capobianco, Senior Director, Technology, Real World Evidence Strategy and Analytics, ICON plc, and Anand Dubey , Senior Business Solutions Architect, Saama Technologies, for this free webinar to learn how ingesting and normalizing a variety of RWD sources is paramount to produce powerful RWE.