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Bringing Predictability and Optimization to the Contracting and Budgeting Process

by Craig Morgan
05Nov2018

Standardized metrics aid in risk management and resource allocation. These tools allow stakeholders to transform data into insights that accelerate study startup, and ultimately clinical trials, through predictability and transparency.

White Paper: Bringing Predictability and Optimization to Contracting and Budgeting Process

The clinical trials sector is awash in metrics, and while early ones generally made broad determinations, such as which sites are top enrollers, the trend is toward more precise metrics that are actionable and help predict where bottlenecks may occur. This is a radical change for stakeholders looking to resolve one of study startup's most enduring challenges — improving budget and contract cycle times.

For years, this laborious step has ranked as the most lengthy of study startup activities, and recent data suggest it remains the primary cause of site activation failure. Some 50.5% of sponsors and 54.3% of contract research organizations (CROs) cite it as the main culprit. Further evidence of contracting problems stems from earlier research, which found that the time period from pre-site visit to contract and budget execution represents the majority of study startup cycle time, 76%, and can average eight months. In addition, a study in which 20,000 contracts were analyzed suggested that site contract cycle times have doubled in recent years. The industry median jumped from 1.5 months in 2010-2011 to more than three months in 2014-2015. With the pharmaceutical industry's intense focus on better performance, stakeholders are ready to embrace strategies that identify and help shorten long contract cycle times.

Fundamental to this change are the growing numbers of forward-thinking organizations who understand how older metrics, often based on a single start and stop time, fail to appreciate what each step entails. To correct this problem, many stakeholders are looking to industry initiatives to break down key benchmarks into their components — the sub-steps — to better identify where bottlenecks might occur. Addressing the budget and contract cycles involves moving away from the single data point approach, such as "time from pre-site visit to execute all site level contracts". This is not a useful metric unless it is based on its sub-steps, such as:

  • When site contract language is ready
  • When first contract language package is sent
  • When last contract language is ready for execution
  • Completion of regulatory submissions and approvals
  • When all contracts are executed

Download our white paper Bringing Predictability and Optimization to the Contracting and Budgeting Process to see why measuring these sub-steps in critical to building predication models that identify processes causing bottlenecks.

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