Full Description
Clinical Research is a fascinating industry, because 99.9% of human beings interact with it throughout their lives. However, very few understand the effort to bring a new medical product to patients in need, and an even smaller number have thought about, or remotely understand, the pivotal role Clinical Data Management (CDM) professionals play in this endeavor. Ranging from sharing hands-on experiences to providing concrete examples of how to propel one's CDM career, Mastering Clinical Data Management: The Background, Experience, and Soft Skills Needed to Succeed in CDM is a glimpse of the author's three-decades-long experience in the field.
Decentralized Clinical Trials (DCTs), Risk-Based Quality Management (RBQM), examining the trustworthiness of the data used in clinical research, or illustrating the use of Artificial Intelligence (AI) in CDM are just a few examples of topics this book not only covers, but also explains for all competency levels through real-life examples. Despite Clinical Research in general, and the profession of CDM in particular, being a heavily regulated and tech-driven environment, this book uses every opportunity to emphasize the importance of the human in the loop. Therefore, in addition to gaining more insights into the fascinating world of CDM, this book provides the perfect "How to" of advancing one's career and learning the art of CDM.
The goal is to provide valuable insights for all levels of CDM professionals and those individuals that might consider a career in CDM.
Key Features
Provides insight into clinical data and its importance as never seen before
Draws on 35 years of hands-on CDM experience
Provides guidance and many examples of crucial soft skills needed to succeed in CDM
Provides arguments for all CDMs to excel in their current work environment
Reflects on the current regulatory framework and how it can benefit CDM
Contents
Introduction and Acknowledgements Chapter 1: The Role of CDM in Clinical Research and Why It Matters Chapter 2: Dissecting Clinical Data to Its "Atomic" Structure. The Case for AI and ML in CDM Chapter 3: Demystifying Risk-Based Quality Management (RBQM) and Why CDM Should Lead It Chapter 4: Technology Overload - How to Avoid Common Pitfalls Chapter 5: Clinical Data Management Versus Clinical Data Science Chapter 6: Demystifying DCTs (Decentralized Clinical Trials) Chapter 7: Stepping Out of the Shadow Chapter 8: Becoming a Clinical Data Science Advocate Chapter 9: From Programmer to Executive - A Personal Story
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