eSource Implementation Consortium

While it would be presumptuous to attempt to predict what 2030 is about, there are clear signals that it would be vastly different than today. The changes in clinical research approaches, the rapid advances in technologies and the generalization of risk-based approaches endorsed by regulators have all started to reshape our discipline. Some organizations have already successfully embarked into the Artificial Intelligence journey and expect to release data review tools powered by Machine Learning in 2021 (i.e., TOMORROW!). If it was not enough, COVID-19 has added fuel to the fire by accelerating the decentralization of clinical trials. This trend is global and EMEA cannot dodge the bullet. There is no going back, we have past the inflection point leading us toward an exciting but different future. Join the SCDM Innovation Committee Chair if you want to better understand what is looming in front of us.

This topic will focus on Risk Based Management from a variety of angles including the quality of the data produced.

Submissions should focus on matters related to risk-based approaches and how they impact CDM:

– RBM’s impact on CDM and visa versa

– Risk-based CDM approach

– QMP management

– QMP with source diversification

This topic should cover the methods of handling the collaboration between customer and clinical data management (i.e. CDM and clinical trial teams, CDM and sites, CMD and CRAs, CDM and subjects, etc.).

This topic will focus on Risk Based Management from a variety of angles including the quality of the data produced.

This topic should cover how CDM organization behaves with respect to the CDM community, what are the potential improvements we should work on in the future, what are the lessons learned from past experiences, etc.

This topic should cover real approaches on new technological and innovation approaches not covered in previous topic and/ or coming not necessarily from CDM (e.g. public health management, finance, physics, etc).

This topic should focus on real cases showing how academia/ Academic Research Organizations (ARO), CRO and pharma are handling data integrity for eSource, adaptive trial design, how they can assure quality by design, what are their perspectives about actual and potential consequences on CDM, how role & responsibilities are changing and will change in the transformation from CDM to Clinical Data Scientist, what will be the relationship with ‘other’ groups (ClinOps, medics, etc.).

This topic should cover real procedures on Data Management best practices and metrics for different data sources (wearables, apps, sensors, Big Data), application of ML/AI/Bots/Big Data Challenge in data analysis, Computer System Validation processes for home-grown applications/functionalities, new study designs, use of Real-world data in clinical development.