Following a major acquisition, Bristol-Myers Squibb has access to a rich and complex variety of internal and external data sources to drive future therapeutic research and patient benefit. This talk will discuss challenges and opportunities as we rethink how we manage and provision this data to seamlessly enable computational research.

Advances in multi-omic profiling and ML/AI analytical tools provide the perfect opportunity for development of PK/PD strategies for data driven clinical development. The presentation will focus on PD-derived data analytics in support of indication selection, independent validation of outputs across clinical development spectrum laying the foundation for lean pivotal trial design.

This session will dive into the importance and power of data standardization within the real-world data space. It will then touch on the benefits of transforming data into the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), demonstrate how this approach can be scaled for multi-source network analyses, and address the need for infrastructure tuned for this type of work.

Corporate analytic data systems were driven by traditional data warehousing models for the better part of two decades. Although effective, and cutting edge technologies for the time, change control inefficiencies and a greater need for ad hoc analytics led to the datalake paradigm, in which data is consolidated in, and used from, a largely unintegrated state. We describe an AWS-based datalake implementation that has been applied to, and is now an enterprise system for, clinical operations as well as emerging support for clinical trial analytics. Components enabling ingestion, cataloging, conformance and reporting will be reviewed.

Early phase clinical trials are governed by complex, concurrent and largely manual GCP processes. We have developed a platform to digitalize and automate these processes, to help study management teams to drive and manage their studies to completion with improved efficiency. The platform could help to transform the way that study teams work in the future.

Digital Health is a term used to describe a broad set of digital technologies and capabilities used in healthcare, including wearable devices, mobile health apps, telehealth and telemedicine, and software as a medical device. Physicians and scientists are increasingly leveraging digital health capabilities to enhance disease understanding, prevention, diagnosis, and treatment, with the goal of improving patient outcome through more targeted healthcare delivery. Likewise, patients are increasingly using digital health capabilities to track or manage their health and to make informed decisions about their healthcare options. This talk will provide examples of how digital technologies are evolving and transforming clinical research and practice to deliver precision medicine.

Advances in standardization of observational healthcare data have enabled methodological breakthroughs, rapid global collaboration, and generation of real-world evidence to improve patient outcomes. To ensure confidence in real-world evidence generated from the analysis of real-world data, one must first have confidence in the data itself. The Data Quality Dashboard is an open-source R package that reports potential quality issues in an OMOP CDM instance through the systematic execution and summarization of over 3,300 configurable data quality checks. Assessing and improving the quality of our data will inherently improve the quality of the evidence we generate.

In this presentation we will go over the concept of Real World Data (RWD) and Real World Evidence (RWE), current development status of RWD/RWE in key countries, key considerations and challenges of how to use RWE to inform drug development and support regulatory decision making. Recent European and American RWE case studies will be discussed in this session.