Trial Forge: a systematic approach to making trials more efficient

Randomised controlled trials (RCTs) are at the core of evidence-based healthcare; they offer the fairest way of evaluating healthcare interventions, whether these interventions include medicinal products, devices or services. Thousands of RCTs are completed across the world every year, providing us with evidence that allows us to draw conclusions on how we manage and treat human disease. What is so surprising though is the level of inefficiency that riddles the design and execution of trial processes.

RCTs are not cheap – the average cost of a trial is estimated to be ~£8,500 per participant in the United Kingdom (1), and with thousands beginning each year the slightest delay or problem can impact budgets immensely. Recruitment of participants is a vital component of the trial process, but high-quality evidence to support strategic approaches to recruitment is thin on the ground; largely we rely on anecdotal information from the trial management team. Recent analysis of trials funded by the UK National Institute of Health Research (NIHR) and Medical Research Council (MRC); the cream of the crop in terms of UK public health trials, highlighted that only 55% of trials recruiting between 2002 and 2008 met their recruitment targets, 45% of these trials required at least one funding extension and even then, only 55% of that group went on to meet their recruitment targets (2). It’s important to highlight that the 55% of trials that received no funding extension were still permitted additional time; the cost of time consists of overheads for the sites involved, staff costs and opportunity lost costs – so no extension is entirely free.

Patient recruitment is just one of the key processes that have been identified as open to inefficiency; others include patient retention, staff training, data management and dissemination, and trial closedown. Trial Forge ultimately aims to work across all trial processes.

Click here to read Heidi's full blog on Students 4 Best Evidence.