As the COVID-19 pandemic continues to stretch the U.S. medical system, nearly two-thirds of clinical trials have been delayed or stopped as doctors and hospitals focus on treating people with the virus. eClinical Solutions LLC, a global provider of cloud-based enterprise software and software-driven clinical data services that accelerate drug development, has released a list of core recommendations to sustain clinical research based on its recent joint study with the Tufts Center for the Study of Drug Development (Tufts CSDD).
“This ripple effect is significant for people with conditions ranging from cancer to heart disease to Alzheimers,” says eClinical Solutions CEO Raj Indupuri. “We are losing months of valuable research time for promising therapies, and some potentially revolutionary drugs because clinical trials are being disrupted. It is critical to use advanced data technologies to connect patients and researchers, wherever they are, to collect high-quality information for ongoing drug submissions and get new therapies to market.”
To continue advancing clinical research during the pandemic, life sciences companies should consider taking the following steps:
1. Define an enterprise data strategy
According to the 2019 Tufts-eClinical Solutions Data Strategies & Transformation Study, more than 75% of sponsors rate six key data management duties as “somewhat or extremely time consuming and labor intensive,” making it clear that improving data management strategies is an urgent priority for organizations looking to leverage scientific insights from a multitude of data sources. The study found that companies with formal data strategies perceive the majority of data activities as less difficult, have shorter cycle times, and see their analytical abilities to be more fully developed and mature.
2. Centralize data and automate the data pipeline
With the volume of external data sources most often cited as the cause of prolonged database lock cycle times, streamlining the collection of data will alleviate downstream trial delays. The Tufts-eClinical Solutions study saw a three week increase in the LPLV-DBL (Last Patient Last Visit to Database Lock) cycle time metric-a 40% increase since 2017. Manual efforts in combining data often results in additional data reconciliation work that contributes to these cycle time delays, making it more crucial than ever to implement data pipelines where data is integrated and mapped in an earlier, automated way.
3. Build data sciences and analytics competencies
The increased use of AI and advanced analytics to automate the clinical trial process means building data sciences skills to support these new models will be critical to accelerating drug development. This is why three out of four sponsors are expanding the role of data scientists in their organizations.