White paper

Bringing Predictability and Optimization to the Contracting and Budgeting Process

Standardized metrics aid in risk management and resource allocation

Bringing Predictability and Optimization to the Contracting and Budgeting Process

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.

This white paper examines why measuring sub-steps is critical to building prediction models that identify processes causing bottlenecks. With a predictive capability, grounded in workflow-based metrics, it becomes possible to condense contract cycle times, presenting a significant opportunity for optimizing clinical trial operations.

Download our white paper "Bringing Predictability and Optimization to the Contracting and Budgeting Process" to learn how by technology is allowing stakeholders to transform data into insights that accelerate study startup, and ultimately clinical trials, through predictability and transparency.

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