Resource management and capacity planning for clinical trial sites.

Kesley Tyson, Jillian Harvey, Leila Forney, Daniel Brinton
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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; R 2 = 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.

Abstract Image

Abstract Image

临床试验场所的资源管理和能力规划。
背景:自2020年以来,临床试验注册数量激增30%以上,对熟练协调员的需求显著增加。尽管有这种增长,但由于责任和工作量不断增加,全国仍然缺乏合格的协调员。有效的资源管理是留住员工的关键。虽然安大略协议评估水平(OPAL)有助于量化试验的复杂性,但它忽略了影响协调员生产力的组织结构和预算限制等关键因素。本项目旨在通过将OPAL评分与纵向协调员工作数据相结合,改进资源分配、操作效率和工作满意度,从而减少倦怠和离职。目的:本研究的目的是减少倦怠和离职,最终促进临床试验的整体成功。方法:将2022年6月1日至2022年12月1日期间积极入组的介入研究纳入数据库,并跟踪相应协调员的工作情况。使用适应性方案评估工具对方案进行评分。描述性统计比较方案特征与适应评估得分和跟踪协调员小时数,而学生t检验和单变量分析评估连续变量的差异。线性回归分析评估了适应得分与协调者努力之间的关系。结果:共分析了7个方案:5个(71%)由联邦政府资助,2个(29%)由行业赞助;4项(57%)是行为干预,3项(43%)是药物研究。在行业资助的研究和联邦资助的研究之间观察到显著差异(7.25±1.77 vs 6.45±1.65;P < 0.0001),行为干预与药物研究之间的差异(6.88±1.56∶6.42±1.91;P < 0.0001)。线性回归结果显示,适应OPAL评分显著预测协调时间(β = 77.22;P = 0.01;r2 = 0.78)。结论:自适应协议复杂性评分预测协调者的工作,有助于能力评估和客观的项目分配。与患者的相关性:该项目的研究结果可以为更精确的资源分配提供信息,可能导致更高质量的研究和增强参与者的安全性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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