Data Automation in Prospective Studies and Registries

This research white paper presents OM1's innovative approach to data automation in prospective studies and registries, addressing the challenges of traditional clinical research. It outlines the labor-intensive nature of clinical studies, emphasizing the need for efficient real-world data (RWD) collection as mandated by the 21st Century Cures Act and the FDA's Real-World Evidence (RWE) Program. The document details how OM1 leverages automation and Artificial Intelligence (AI) to streamline data collection, ensuring regulatory compliance while enhancing cost-effectiveness and patient-centricity. The paper describes the dual approach of passive data collection from Electronic Health Records (EHRs) and active data collection for essential elements not routinely captured. It highlights the role of AI in processing structured and unstructured data, maintaining data quality, and meeting regulatory standards. Additionally, the paper discusses the cost efficiency achieved through automation, demonstrating significant reductions in costs per subject as study sizes increase, and the diverse applications of automated registries throughout the medical product lifecycle.