AccScience Publishing / JCTR / Volume 10 / Issue 4 / DOI: 10.36922/jctr.24.00022
ORIGINAL ARTICLE

Resource management and capacity planning for clinical trial sites

Kesley Tyson1 * Jillian Harvey2 Leila Forney3 Daniel Brinton2
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1 Clinical Research Center, Morehouse School of Medicine, Atlanta, Georgia, United States of America
2 Department of Healthcare Leadership and Management, College of Health Professions, Medical University of South Carolina, Charleston, South Carolina, United States of America
3 South Carolina Clinical and Translational Research Institute, College of Medicine, Medical University of South Carolina, Charleston, South Carolina, United States of America
JCTR 2024, 10(4), 229–236; https://doi.org/10.36922/jctr.24.00022
Submitted: 31 May 2024 | Accepted: 31 July 2024 | Published: 20 August 2024
© 2024 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Abstract

Background: Since 2020, the number of registered clinical trials has surged by over 30%, significantly increasing the demand for skilled coordinators. Despite this growth, a national shortage of qualified coordinators remains, driven by escalating responsibilities and workloads. Effective resource management is crucial for retention. While the Ontario Protocol Assessment Level (OPAL) helps quantify trial complexity, it overlooks key factors such as organizational structure and budget constraints that impact coordinator productivity. This project aims to refine the OPAL score by integrating it with longitudinal coordinator effort data, improving resource allocation, operational efficiency, and job satisfaction, thereby reducing burnout and turnover.

Aim: The aim of this study was to reduce burnout and turnover, ultimately contributing to the overall success of clinical trials.

Methods: Actively enrolling interventional studies with corresponding coordinator effort tracking from June 1, 2022, to December 1, 2022, were included in the database. Protocols were graded using an adapted protocol assessment tool. Descriptive statistics compared protocol characteristics to the adapted assessment score and tracked coordinator hours, while Student’s t-test and univariate analysis evaluated differences in continuous variables. Linear regression analysis assessed the association between the adapted score and the coordinator effort.

Results: Seven protocols were analyzed: five (71%) were federally funded, two (29%) were industry-sponsored; four (57%) were behavioral interventions, and three (43%) were drug studies. Significant differences were observed between industry-sponsored and federally funded studies (7.25 ± 1.77 vs. 6.45 ± 1.65; P < 0.0001) and between behavioral interventions and drug studies (6.88 ± 1.56 vs. 6.42 ± 1.91; P < 0.0001). Linear regression revealed the adapted OPAL score significantly predicted coordinator hours (β = 77.22; P = 0.01; R2 = 0.78).

Conclusion: The adapted protocol complexity scores predict coordinator effort, aiding in capacity assessment and objective project distribution.

Relevance for Patients: The findings from this project can inform more precise resource allocation, potentially leading to higher-quality studies and enhanced participant safety. 

Keywords
Protocol complexity
Workloads
Ontario Protocol Assessment Level
OPAL score
Coordinator effort
Historically black college and university
Conflict of interest
The authors declare that there are no conflicts of interest to disclose.
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Journal of Clinical and Translational Research, Electronic ISSN: 2424-810X Print ISSN: 2382-6533, Published by AccScience Publishing