Patrick Nadolny

This position paper clarifies key CDS concepts and provides insights on how CDM professionals can efficiently set their path toward CDS and how they actually can make it happen.

While this paper is not meant to be an exhaustive change management guide, it provides a concrete set of recommendations on how to evolve an organization toward CDS or simply build/rebuild a CDM organization.

Background: In 2016, the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use updated its efficacy guideline for good clinical practice and introduced quality tolerance limits (QTLs) as quality control in clinical trials. Previously, TransCelerate proposed a framework for QTL implementation and parameters. Historical data can be important in helping to determine QTL thresholds in new clinical trials

This paper outlines an industry perspective on maximizing the value of implementing the targeted, routine review of these extremely large datasets. It provides recommendations on risk-based use cases for audit trail review (ATR) and the corresponding desired reporting criteria, with suggestions on when to use visualizations and exception report listings to generate key, actionable insights. It contains practical implementation guidance, covering the people, processes and technology needed to… Voir plus

This paper outlines an industry perspective on maximizing the value of implementing the targeted, routine review of these extremely large datasets. It provides recommendations on risk-based use cases for audit trail review (ATR) and the corresponding desired reporting criteria, with suggestions on when to use visualizations and exception report listings to generate key, actionable insights. It contains practical implementation guidance, covering the people, processes and technology needed to execute ATRs effectively. The authors also take a deep dive into the technical aspects of what constitutes an audit trail, and how collecting and preparing audit trail data is fundamental to successful ATR capability. Voir moins

This article will share insights on how to pragmatically initiate an AI journey using the evolution of clinical data management as an example.

This paper provide insights on how CDM professionals who have successfully and passionately contributed to the credibility of CDM can evolve their skillsets and competencies to cope with the increasing complexities of clinical research which demands novel approaches maximizing the potential of available technologies.

TransCelerate BioPharma Inc.’s interpretations of Clinical Guidances & Regulations Initiative’s paper on a proposed definition of a #QTL, process map/steps and examples of QTL parameters

In seeking to build upon the previous publication on the industry trends on the evolution of Clinical Data Management to Clinical Data Science, this second chapter focuses on the technologies enabling this evolution and allowing organizations to efficiently manage the 5Vs of clinical data (i.e., Variety, Volume, Velocity, Veracity, and Value).

This second paper also shares insights and lessons learned from leaders, pioneers and early adopters of those emerging technologies.

The Evolution of Clinical Data Management to Clinical Data Science: A Reflection Paper on the impact of the Clinical Research industry trends on Clinical Data Management. In the context of this reflection paper, Clinical Data Science is defined as the strategic discipline enabling data driven Clinical Research approaches and ensuring subject protection as well as the reliability and credibility of trial results. Clinical Data Science encompasses processes, domain expertise, technologies, data… Voir plus

The Evolution of Clinical Data Management to Clinical Data Science: A Reflection Paper on the impact of the Clinical Research industry trends on Clinical Data Management. In the context of this reflection paper, Clinical Data Science is defined as the strategic discipline enabling data driven Clinical Research approaches and ensuring subject protection as well as the reliability and credibility of trial results. Clinical Data Science encompasses processes, domain expertise, technologies, data analytics and Good Clinical Data Management Practices essential to prompt decision making throughout the life cycle of Clinical Research. Voir moins

This article focuses on detecting data quality issues, irrespective of origin or motive. Early detection of data quality issues are important so that corrective actions taken can be implemented during the conduct of the trial, recurrence can be prevented, and data quality can be preserved.

The manuscript examines the value of Source Data Verification (SDV) and concludes that the SDV has limited value as a quality control measure. TransCelerate's RBM methodology proposes shifting monitoring processes from an excessive concentration on SDV to comprehensive, risk-driven monitoring that uses a combination of central, off-site and on-site monitoring activities.

The eClinical Forum Risk Based Monitoring Taskforce offers some best practices for ensuring clinical data quality