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Realising the full potential of data-enabled trials in the UK: a call for action

Authors :
Andrea Cipriani
Steve Goodacre
Jennifer K Quint
Catherine Hewitt
Ian Roberts
John Norrie
Aziz Sheikh
Michael King
Irwin Nazareth
Alan Watkins
Frank Sullivan
Derek Stewart
Nick Freemantle
Liam Smeeth
Jane Daniels
Tobias Dreischulte
Brooke Jackson
Michael Donnelly
Martin Landray
Martin Gulliford
Mark Edwards
Paula R Williamson
David A Harrison
Adam Streeter
Jennifer Logue
Paula Williamson
Louise Bowman
Marion Campbell
Martin C Gulliford
Jamie Soames
Dheeraj Rai
Stephanie MacNeill
Marion Bennie
Jo Rycroft-Malone
Matthew R Sydes
Andrew Farmer
Hywel C Williams
Martyn Lewis
Elizabeth Williamson
Xavier Griffin
Colin McCowan
John Wilding
Jennifer Quint
Agnieszka Michael
Murali Shyamsundar
Tom Denwood
Deborah Ashby
Jacqui Nuttall
Kate Williams
Doreen Tembo
David Harrison
Andrew Morris
Laura Gray
Fiona Lobban
Claire Snowdon
Martin Gibson
Rhoswyn Walker
Will Whiteley
Nick Mills
Juliet Tizzard
Emer Brady
Guillermo Lopez Campos
Catrin Tudur Smith
Yolanda Barbachano
Steph Garfield-Birkbeck
Will Navaie
Martin O'Kane
Jonathan Sheffield
Paula Walker
Rhoswyn R Walker
Janet Valentine
Susan Beatty
Helen Bodmer
Paul Brocklehurst
Steph Garfield‑Birkbeck
Doug Gould
Thomas Hiemstra
Anna Higgins
Julia Hippisley‑Cox
Sasha Korniak
Marion Mafham
Kathleen Meeley
Chris Newby
Martin O’Kane
Mike Robling
Jo Rycroft‑Malone
Matthew Sydes
Hwyel Williams
Source :
BMJ Open, Vol 11, Iss 6 (2021)
Publication Year :
2021
Publisher :
BMJ Publishing Group, 2021.

Abstract

Rationale Clinical trials are the gold standard for testing interventions. COVID-19 has further raised their public profile and emphasised the need to deliver better, faster, more efficient trials for patient benefit. Considerable overlap exists between data required for trials and data already collected routinely in electronic healthcare records (EHRs). Opportunities exist to use these in innovative ways to decrease duplication of effort and speed trial recruitment, conduct and follow-up.Approach The National Institute of Health Research (NIHR), Health Data Research UK and Clinical Practice Research Datalink co-organised a national workshop to accelerate the agenda for ‘data-enabled clinical trials’. Showcasing successful examples and imagining future possibilities, the plenary talks, panel discussions, group discussions and case studies covered: design/feasibility; recruitment; conduct/follow-up; collecting benefits/harms; and analysis/interpretation.Reflection Some notable studies have successfully accessed and used EHR to identify potential recruits, support randomised trials, deliver interventions and supplement/replace trial-specific follow-up. Some outcome measures are already reliably collected; others, like safety, need detailed work to meet regulatory reporting requirements. There is a clear need for system interoperability and a ‘route map’ to identify and access the necessary datasets. Researchers running regulatory-facing trials must carefully consider how data quality and integrity would be assessed. An experience-sharing forum could stimulate wider adoption of EHR-based methods in trial design and execution.Discussion EHR offer opportunities to better plan clinical trials, assess patients and capture data more efficiently, reducing research waste and increasing focus on each trial’s specific challenges. The short-term emphasis should be on facilitating patient recruitment and for postmarketing authorisation trials where research-relevant outcome measures are readily collectable. Sharing of case studies is encouraged. The workshop directly informed NIHR’s funding call for ambitious data-enabled trials at scale. There is the opportunity for the UK to build upon existing data science capabilities to identify, recruit and monitor patients in trials at scale.

Subjects

Subjects :
Medicine

Details

Language :
English
ISSN :
20200439 and 20446055
Volume :
11
Issue :
6
Database :
Directory of Open Access Journals
Journal :
BMJ Open
Publication Type :
Academic Journal
Accession number :
edsdoj.084d24ef9a4402eb00d06037f78d6f0
Document Type :
article
Full Text :
https://doi.org/10.1136/bmjopen-2020-043906