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

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.

Use of Proxy Variables to Determine the Impact of Protocol Complexity on Clinical Research Site Productivity.

BACKGROUND: Research coordinators (or teams) are usually assigned to multiple studies of varying complexity at any one time, each with different and ever-changing workloads. As a result, determining the impact of protocol complexity on productivity is not easily accomplished. Standard methods of effort tracking typically require oversight or create additional workload to the site staff under study; they are time-consuming, expensive, intrusive, and usually incomplete. METHODS: This article describes a novel method for determining the impact of protocol complexity on clinical research coordinator (CRC) or team productivity by using proxy variables in place of effort tracking. A protocol assessment tool that quantitates complexity is used to determine cumulative workload. RESULTS: Productivity graphs are generated for each CRC per month and can be followed over time to assess trends or for comparative analysis. CONCLUSION: The data provide managers with unique insights into the functional capacity of study coordinators and support staff. The goal is to optimize efficiency by applying a systematic decision process from performance and productivity trends. In addition to exploring the theory behind the method, this article begins a discussion on the use of this information in clinical research site management.