Harnessing Real-World Data for Regulatory Use and Applying Innovative Applications

Abstract

Kelly H Zou,1 Jim Z Li,2 Joseph Imperato,3 Chandrashekhar N Potkar,4 Nikuj Sethi,5 Jon Edwards,6 Amrit Ray7 1Research, Development and Medical, Upjohn Division, Pfizer Inc, New York, NY 10017, USA; 2Research, Development and Medical, Upjohn Division, Pfizer Inc, San Diego, CA 92121, USA; 3Business Technology, Upjohn Division, Pfizer Inc, New York, NY 10017, USA; 4Research, Development and Medical, Pfizer Gulf FZ LLC, Dubai Media City, United Arab Emirates; 5Business Technology, Upjohn Division, Pfizer Inc, Collegeville, PA 19426, USA; 6Envision Pharma Group, Envision House, Horsham RH12 1XQ, UK; 7Research, Development and Medical, Upjohn Division, Pfizer Inc, Collegeville, PA 19426, USACorrespondence: Kelly H ZouVice President and Head of Medical, Analytics and Insights, Research, Development and Medical, Upjohn Division, Pfizer Inc, 235 East 42nd Street, MS 235-9-1, New York, NY 10017, USAEmail [email protected]: A vast quantity of real-world data (RWD) are available to healthcare researchers. Such data come from diverse sources such as electronic health records, insurance claims and billing activity, product and disease registries, medical devices used in the home, and applications on mobile devices. The analysis of RWD produces real-world evidence (RWE), which is clinical evidence that provides information about usage and potential benefits or risks of a drug. This review defines and explains RWD, and it also details how regulatory authorities are using RWD and RWE. The main challenges in harnessing RWD include collating and analyzing numerous disparate types or categories of available information including both structured (eg, field entries) and unstructured (eg, doctor notes, discharge summaries, social media posts) data. Although the use of artificial intelligence to capture, amalgamate, standardize, and analyze RWD is still evolving, it has the potential to support the increased use of RWE to improve global health and healthcare.Keywords: real-world data, real-world evidence, regulatory, artificial intelligence, robotic process automation

Keywords

• real-world data
• real-world evidence
• regulatory
• artificial intelligence
• robotic process automation