TMF Summit Europe 2019: Montrium CEO Paul Fenton to Present on Data-Driven eTMF
This year’s 8th Annual European TMF Summit will focus on the importance of integrating technology, processes, and multiple points of view for attendees to better understand how to improve on their current trial master file (TMF) system. The compilation of the TMF can be one of the most challenging activities within a clinical trial, with thousands of different documents and records that need to be produced in a timely, compliant manner.
To ease frustrations and promote collaboration, electronic TMFs give sponsors and CROs the ability to contribute and access important content in real-time within a centralized and standardized environment. This year, topics at the conference will cover how to improve eTMF workflow through interoperable technologies, use disruptive technologies to improve metrics reporting and efficiency, and put controls in place to meet oversight and monitoring requirements of ICH E6(R2) using a risk-based approach.
When: October 1-3, 2019
Where: London Marriot Hotel Regents Park, London, UK
What: During the conference, Montrium will be showcasing eTMF Connect, our eTMF solution which helps life sciences companies better manage their clinical trial documentation. Designed using the TMF Reference Model, eTMF Connect allows teams to create and manage all of the essential documents required to streamline their clinical trial management.
Montrium’s CEO Paul Fenton will be presenting a Case Study on “Data-Driven eTMF — Towards Improved Quality, Timeliness, and Completeness” (Wednesday, October 2nd, 2019 at 13:45)
The presentation will cover how to:
Ensure quality, timeliness and completeness of eTMF by using operational data to interconnect clinical processes, activities and events
Gain visibility of what artefacts are required and the lack of triggers present to prompt contributors
Employ operational data and data management strategies to ensure the highest quality eTMF
Improve how we predict and manage artefacts through the use of predictive analytics based on central process models and data management techniques