A Stepwise Approach to Data-driven Study Start-up
COVID-19 has highlighted the urgent need for greater efficiency and streamlined approaches in clinical drug development. Study start-up is an area that’s time and resource-intensive and has been predominantly managed through hundreds of spreadsheets and manual methods for the last 15+ years.
Study start-up is an area that could greatly benefit from automation, as start-up activities account for 61% of total clinical trial cycle times.1 With site initiation averaging 31.4 weeks and site contracts taking up to 13 weeks to execute,2 it’s not surprising that nearly 40% of sponsors and CROs are dissatisfied with current site initiation processes.3
As the industry looks for ways to optimize clinical processes in the post-COVID environment, study start-up has emerged as a key focus area. We’ve heard sponsors and CROs enthusiastically say in recent discussions they want more automation and industry benchmarking to transform start-up processes. While this surge in interest in clinical technologies is great for the industry, the move from spreadsheets to data-driven study start-up requires a methodical approach and a thoughtful strategy to achieve success.
A data-driven approach doesn’t necessarily translate to more efficient site selection and faster study start-up, especially when companies examine the various data sources that inform activities. Many organizations utilize various internal systems, lists, and external third-party sources such as investigator databases, registries, data warehouses, Citeline Informa, and other trial intelligence services, but not all data sources are created equal or provide the same level of value. Challenges that typically arise with some external data sources include:
- Incomplete or niche data doesn’t provide a complete, accurate view of site performance data,
- “Unclean” data,
- Conflicting data between sources,
- Subscription services that provide industry benchmarking data lack granular details and data quality can be questionable.
By taking a methodical approach to data analysis, sponsors and CROs can identify those sources that provide meaningful insights into their organization’s performance.
As companies look to enhance their study start-up processes, it’s important to also consider baseline metrics. Industry benchmark data is certainly important, but only if you are aware of what your organization can achieve. Accessing site and investigator lists used by industry organizations to drive high-quality site selection is meaningful when you have a clear understanding of the strengths and gaps in your company’s site selection processes.
While start-up operations and activities can be largely predictive and automated, you can take a stepwise approach to transformation and still get immediate value and efficiency gains. People, process, and technology are all important pillars that require coordination and alignment.
How can you lead the change initiative in your organization to improve study start-up? Stay tuned for the next blog in our series for a deeper dive into phase one – process optimization approaches and the role of unified systems.
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1. Lamberti, Mary Jo. Tufts Center for the Study of Drug Development Impact Report. March 2018.
2. Society for Clinical Research Sites. Common Language Evaluation and Reconciliation (CLEAR).
https://myscrs.org/common-language-evaluation-and-reconciliation-clear/
3. Lamberti, Mary Jo. Tufts Center for the Study of Drug Development Impact Report. March 2018.