Harmonizing Protocol Complexity with Resource Management and Capacity Planning at Clinical Research Sites

Abstract

Background

Clinical research sites conduct trials with diverse complexities, timelines, and ever-changing workloads. Though the principal investigator (PI) is ultimately responsible for the content and conduct of trials, they rely heavily on site staff to successfully enroll and complete studies following good clinical practice (GCP) Guidelines. The mainstays of the site workforce are the clinical research coordinators (CRCs) to whom the trials are assigned. These CRCs work on many studies concurrently. Managing study assignments and workload is a difficult task that requires knowledge of the trial complexity, expected enrollment, and many other factors affecting performance.

Methods

Traditional methods for allocating workload to site staff quantitate trial complexity and estimate work hours by factoring in the number of trial participants. However, this does not account for the effects of associated workload or variability in staff attributes. It also neglects other factors that affect performance and assumes maximum enrollment and completion of the trial by all participants. This article introduces a novel approach that determines the effects of protocol complexity on CRC productivity without effort tracking. These metrics permit an assessment of how the CRC’s performance is affected by the number of studies assigned.

Results

By understanding the effects of workload allocation on CRC productivity and capacity, the site manager can use an algorithmic approach toward improving performance. The process takes into account factors that are both within and outside the control of the site manager.

Conclusion

Sites may benefit from analytics that measures how CRCs adapt to the effects of study complexity on cumulative workloads over time. Optimizing productivity also means conforming to GCP Guidelines and avoiding staff burnout. As studies become increasingly difficult, site managers need tools to manage complexity and balance workloads among staff.