Modern clinical trials gather an unprecedented amount of data that allows for more informed and comprehensive scientific research.
Driven by wearable devices, digital health technologies, biospecimens and more, each trial produces an astonishing 3.6 million data points on average.
This surge in data is a blessing; more data provides deeper insights into side-effects, efficacy, comorbidities and other factors, but it can also become a challenge.
The life sciences industry is finding it difficult to manage these vast and complex datasets, leading to costly delays and slower decision-making.
When trials are not designed with data standardisation and workflow optimisation in mind, massive amounts of fragmented records can easily overwhelm and lead to chaos and confusion when collating, organising and analysing for regulatory submission or to inform subsequent phases in research.
However, with recent advances in AI and its powerful automation capabilities, paired with a renewed focus on aligned data collection standards, clinical trial data workflows don’t have to be burdensome and complex.
Martin Snyder (pictured), President of Certara Data Sciences, reports.
Not yet a Subscriber?
This is a small extract of the full article which is available ONLY to premium content subscribers. Click below to get premium content on Manufacturing Chemist.
Subscribe now Already a subscriber? Sign in here.