The future of research in hematology: Integration of conventional studies with real-world data and artificial intelligence. (July 2022) - Passamonti, Francesco; Corrao, Giovanni; Castellani, Gastone; Mora, Barbara; Maggioni, Giulia; Gale, Robert Peter; Della Porta, Matteo Giovanni
Abstract: Most national health-care systems approve new drugs based on data of safety and efficacy from large randomized clinical trials (RCTs). Strict selection biases and study-entry criteria of subjects included in RCTs often do not reflect those of the population where a therapy is intended to be used. Compliance to treatment in RCTs also differs considerably from real world settings and the relatively small size of most RCTs make them unlikely to detect rare but important safety signals. These and other considerations may explain the gap between evidence generated in RCTs and translating conclusions to health-care policies in the real world. Real-world evidence (RWE) derived from real-world data (RWD) is receiving increasing attention from scientists, clinicians, and health-care policy decision-makers - especially when it is processed by artificial intelligence (AI). We describe the potential of using RWD and AI in Hematology to support research and health-care decisions. Highlights: Entry-criteria for RCTs often fail to reflect the population meant to use an intervention. Seemingly similar RCTs sometimes contradictory conclusions. Electronic health records (HER) are a value source of RWD to develop RWE. RWE can inform utility and applicability of data from RCTs. AI increases meaningful RWE output and accelerates developing insights.