Harmonizing Protocol Complexity with Resource Management and Capacity Planning at Clinical Research Sites
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.
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.
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.
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.
KeywordsComplexity Workload Capacity Productivity Resources
No financial support for the research, authorship, and publication of this article was declared.
This article does not address the performance issue regarding having a CRC who is optimally allocated but is not productive. That scenario is the subject of future article exploring the root cause of failure.
Compliance with Ethical Standards
Declaration of Conflicting Interests
The author is the Founder, CEO, and principal owner of Trike®, LLC, developer of SiteOptex® Software, Quantified Efficiency Methodology, Patent Pending.
- 1.International Council of Harmonization (ICH) E6 (R2). Guideline for Good Clinical Practice, Section 4.2.Google Scholar
- 2.Patent Pending: Quantified Efficiency Methodology, Morin, David Joseph.Google Scholar
- 3.Productivity and Productivity Calculations for Business. Published 17 Apr 2015. http://www.shmula.com/process-measures-productivity-and-productivity/319/.
- 6.Hartigan M, Doyle K. Effort tracking—is it worth the effort? In: Paper presented at PMI® Global Congress 2007—EMEA, Budapest Hungary, Newtown Square: Project Management Institute.Google Scholar
- 7.Morin DJ. Use of proxy variables to determine the impact of protocol complexity on productivity. Ther Innov Regul Sci. 2018;53(1):52–58. Republished 1 Jan 2019.Google Scholar
- 8.Morin DJ, Gilbert K. Determining optimal performance at the clinical research site using the Tool for Operational Protocol Scoring (TOPS©). Clin Res. 2015;June:52–57 for reprints contact: firstname.lastname@example.org. (TOPS©) copyright by Karen L. Gilbert.Google Scholar