Drug Discovery and Big Data: From Research to the Community

Technology and artificial intelligence, alongside the COVID-19 pandemic vastly increasing technology use in health care, have precipitated an escalation of big data. Although real-world data (RWD) and real-world evidence (RWE) have contributed to determining outcomes outside the scope of randomized clinical trials (RCTs), RWD and RWE are underutilized in demonstrating drug effectiveness. Utilizing RWD may enhance the ability of regulatory agencies to approve drugs, provide drug effectiveness insight to payers, and improve personalized medicine. Additionally, RWD and RWE may assist in overcoming the limitations of RCT data such as treatment adherence and underrepresented patient subgroups and may support and expedite drug repositioning. Even though the limitations of using RWE and RWD include fragmented data context, poor data quality, and information governance, healthcare analytics hubs such as the European Health Data Space are designed to foster synergy among private and public healthcare players and may assist in overcoming these potential limitations. Such healthcare analytics hubs may enhance the utilization of RWE and/or RWD, which could ultimately result in better patient outcomes. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Literature review of data analytics utilization in Supply Chain Management

With an exponential increase in transition of various business to technical platforms there has been a huge data transfer between various platforms occurring every second. In 2020, there was a significant increase in the number of businesses shifting to online platforms during COVID, which not only increased the data traffic but also led both academic and IT industry to think of various techniques to improve data quality and data transformations in order to achieve optimal solutions from their captured data. The current review paper provides insight about how data analytics has been used by various phases within Supply chain management and also addresses the usage of data analytics in creation of various SCM models. The paper also addresses a number of research gaps while creating these SCM models, which leads to future research directions. © 2023 IEEE.