Newsletter January 2020

Risk based monitoring

Risk-based monitoring refers to a series of monitoring processes that include the presumed risks that could affect the quality or safety of a study, advised by Health Authorities (ICH GCP Guideline E6[R2]) for responding better to trials’ main challenges and priorities.

CROs accept that each study demands its own risk-based monitoring processes, which is why they emphasize on the preliminary steps (trial, protocol and database design) before the study conduction for identifying possible risks and putting in place an adapted monitoring.

Traditionally, the sponsor got data gathered from different sites, and it was 100% verified. RBM strategies are changing traditional clinical study conduction. Thanks to the availability of sources off-site and telecommunication technologies, the study coordinator is not only able to remotely monitor subjects and sites’ data but also to deeply analyze, remotely, the alerts and to manage them. It encourages and allows the decision making "as soon as possible".

Among the different RBM strategies recommended by the FDA and EMA, we can retain 4:

  • Reduced source document verification
  • Targeted
  • Remote
  • Centralized monitoring

Different tools need to be used to implement RBM within a trial such as: statistics, strong predictive and advanced analytics and it can also integrate AI and ML

Artificial Intelligence and Machine Learning in RBM

AI and ML have impacted the way of conceiving Human tasks and roles. Many consider that new technologies are helping professionals to focus on what really matters by providing them powerful analytics tools.

In Clinical trials and RBM, new technologies are not only helping reduce the source data verification time but to also quickly identify risks on subject exposure, level of AE risks on sites, protocol risks, false positive/negatives… This helps protect patients’ safety and enhance their quality of life.

The use of AI and ML in RBM relies, among others, on the use of smart risk dashboards (created at study-design phase) whose alerts integrate data mining, smart and predictive graphs and other data science tools to indicate situations that need further investigation.

Although we might be aware of new and evolving technologies' power, can we say that in some years, studies will be fully conducted by machines?

Gregg Larson, VP of Clinical Operations at Ultragenyx Pharmaceuticals Inc., was interviewed by the DIA's forum on November 2019, and he stressed on the importance of Human critical thinking when using new technologies for RBM. He said that "fully relying on algorithms can be misleading" and declared that the role of new technologies is mainly take the "checklist" tasks’ place, thanks to its ability of fast processing.

The role of Clinical research associates is mainly evolving to the climax of its capacity on being judgemental when facing data for critical decision-making. The interviewee insisted on the skill of staying "vigilant on focusing on what matters the most".

As AI and ML, along with new technologies, are still evolving, we can expect changes and greater integration of these technologies within RBM on Clinical trials.

And you, what do you think about new technologies being used for RBM on Clinical trials?


Save-the-date, conferences and events to come

DIA Europe 2020

17-19 March 2020 - Brussels

  • Artificial Intelligence and Case Studies
  • The challenges from the future for the EU Policy and Regulatory Framework

Click here for registration and more details

ACDM Annual Conference 2020

8-10th March 2020 - Clontarf Castle Hotel in Dublin

Winners of the ACDM Awards 2020 will be announced (Individual Excellence in the Management of Clinical Data, Team Excellence in the Management of Clinical Data, Innovation in the Management of Clinical Data)

9ème Journée de la Recherche Clinique

January 30, 2020 - Paris

This meeting brings together all stakeholders, professionals and actors in Clinical and Epidemiological Research

Click here for more details

SCDM Interesting White Paper

Reflection paper that details the industry transformation from Clinical Data Management to Clinical Data Science. This is a must-read.
The information in this paper defines what it will look like to be in our industry in the next years

Click here to read


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