Clinical trial complexity continues to increase due to vast accumulations of data, targeted therapies, regulatory requirements, and patient recruitment challenges, all of which can lead to trial delays. But the stakes for delays in clinical research are much higher than in other industries—for some patients, a trial delay can be the difference between life or death; for sponsors, it can also mean millions of dollars wasted.
- Approval one year earlier of a breakthrough HIV/AIDS drug that extended lifespans 14 years would have added $19 billion in patient economic benefits and $3.7 billion in additional sponsor profits1
- Clinical approval delays can cost as much as $600,000 in lost sales per day for niche products and $8 million per day for blockbuster drugs2
Sponsors want relevant data sources with applied AI and machine learning techniques to drive scientific and operational insights, to answer complex questions, and to ensure operational excellence in clinical trial execution. The lack of these services requires a large amount of resources to manually gather and assimilate data for individuals with varied scientific knowledge to make decisions.
Sponsors contend with a lack of current and relevant data, and they face time delays due to the significant amount of manual effort needed to combine and format data.
This piecemeal approach often arises from internally developed IT, uses study-specific data source forms created in a spreadsheet, and usually requires manual transcription. A fragmented approach to automation actually adds complexity to the trial process—it delays trial reporting, prevents rapid identification of problems, and cannot support real-time data quality management.
Bottom line: A fragmented approach results in mounting study delays and costs:
- Up to 25 discrete systems used in a single trial1
- 75% of studies can’t integrate their tools leading to disjointed workflows2
- 50+ business days average time to build and release clinical study database3
So what’s the path forward?
The most effective way to curb clinical trial complexity is a flexible, comprehensive clinical and business platform approach. This type of unified trial platform includes access to a massive, detailed clinical and business database, automated and manual data validation and standardization, a flexible, scalable unified interface, powerful analytics, and user-friendly technology.
Adopting one platform to unify data and workflows across study execution allows for rapid study startup and streamlined execution with cleaner data, ultimately eliminating unnecessary reconciliation efforts and resulting in faster closeout.
Sponsors and CROs realize immediate benefits in the study startup and EDC buildout by reducing redundant setup efforts in multiple tools and eliminating the need for awkward integrations:
- 40% reduction in data correction rates
- 26% faster enrollment with centralized data capture
- 64% faster data capture with unified RTSM / IRT and ePRO
- Sun, E and Philipson, TJ. (2010). Cost of Caution: The Impact on Patients of Delayed Drug Approvals. Retrieved from the Manhattan Institute website: https://www.manhattan-institute.org/pdf/fda_02.pdf
- Cutting Edge Information. (2004). Accelerating Clinical Trials: Budgets, Patient Recruitment and Productivity. Retrieved from Cutting Edge Information website.