3 Ways to Accelerate Study Start-Up in Life Sciences
Study Start-Up (SSU) is one of the most critical, yet inefficient, steps in the clinical trial process.
Clear information and rapid, collaborative input is required for start-up, but heavily siloed data, unreliable and highly variable processes, and complex global regulations are impeding efficiency.
Delays in the SSU process impact a product’s approval and launch, raising the cost of a clinical trial and delaying the delivery of life-altering, or even life-saving, medicines to patients.
Effective study start-up depends on speed, efficient processes, and complete accuracy. A digital transformation platform is required to provide clear oversight, fast action, and a high degree of accuracy during the study start-up process.
When selecting an SSU solution, life sciences organizations should look for three important capabilities.
1. Bridging Siloed Data
Life Sciences organizations tend to disperse data and process among several disjointed systems. Some organizations even develop separate study start-up teams that operate in an isolated environment and then must hand off their processes to other departments within the company. These breaks in data and communication flow paired with the complexity of the clinical study process, contribute to a highly inefficient study start-up.
A platform that can bring together disjointed systems will break down communication barriers among team members spread across different sites, opening collaboration abilities. Gaining a single view of all the data needed for SSU increases process efficiency and allows the study start-up team to move forward at a faster pace.
2. Automating Manual Processes
The life sciences industry predominantly relies on paper-based or low-tech systems to execute a study start-up. These manual processes slow down execution and do not offer real-time visibility into a study’s progress. Human error is also a concern when using manual processes. Mistakes during this phase of R&D can interfere with gaining compliance and heighten risk for the patient.
To reduce error and lower risk in SSU, it is crucial to automate these manual processes. Using machine learning technology to automate redundant processes allows organizations to streamline the study start-up. A system that provides real-time updates and automated tracking brings added transparency that mitigates risk and provides insights for proactive decision making.
3. Meeting Regulatory Standards
Operating across different systems, sites, and regions around the globe can prove challenging when trying to maintain regulatory requirements. SSU organizations must comply with regionally varying and ever-changing regulations. Working with disjointed systems while manually assuring best practices is prone to error and time consuming.
To be successful, an organization needs to utilize an application that enforces consistent processes and standard operating procedures (SOPs) across all geographies and therapeutic areas, while offering the ability to customize for unique local situations.
Unlock Patient Value
Digitally transforming these 3 areas of SSU provides life sciences organizations with the opportunity to streamline processes and ultimately solve their study start-up challenge once and for all. However, the benefits of digital transformation for life sciences do not end with study start-up. To see how Appian’s platform is unlocking patient value across the product lifecycle, visit our Life Sciences resource center.
And be sure to meet with Appian if you will be participating at the 30th Anniversary of DIA Europe on 17-19 April in Basel, Switzerland. This conference will bring together leaders in the Life Sciences industry to discuss the most innovative approaches to Study Start-Up, as well as diving deeper into other areas such as Regulatory Science, Patient Engagement, and Pharmacovigilance. Schedule a meeting with an Appian Executive to further discuss how Appian’s capabilities can help unlock patient value for Study Start-Up and across the Life Sciences product lifecycle.
Industry Leader, Life Sciences