Holmes Kesley, Idris Muhammed, Harvey Jillian, Forney Leila, Brinton Daniel, Billingslea Jan Morgan, Pemu Priscilla
Clinical Research Center, Morehouse School of Medicine, Atlanta, Georgia, United States of America.
Department of Healthcare Leadership and Management, College of Health Professions, Medical University of South Carolina, Charleston, South Carolina, United States of America.
J Clin Transl Res. 2025 Aug 25. doi: 10.36922/jctr025260032.
The escalating complexity of clinical trial protocols has considerably increased the workload for research coordinators, exacerbating staffing shortages and contributing to operational inefficiencies. These challenges are particularly pronounced at under-resourced and minority-serving research institutions, where limited capacity may hinder the implementation of trials. Early and accurate estimation of research coordinator effort is essential for effective planning, resource management, and successful clinical trial conduct.
This study assesses the accuracy of an adopted Ontario Protocol Assessment Level (OPAL) score in predicting coordinator workload to improve operational planning in clinical research.
A prospective observational study was conducted over a 12-month period at a Historically Black College and University medical school. Seven coordinators recorded hours for seven actively enrolling interventional trials. Estimated workloads were calculated using a published, adapted OPAL reference table, and were compared with actual hours using descriptive statistics and paired -tests. To ensure consistent benchmarking, workday equivalencies (7.5 h for institutional standards and 8 h for industry standards) were applied.
There was no statistically significant difference between estimated and actual hours, with an average difference of 24.1 h (=0.761). The mean absolute error was 167.0 h, equivalent to roughly 1 month of full-time work.
The adapted OPAL score provides a practical tool for estimating coordinator workload and aligning staffing with protocol complexity, including in under-resourced settings. However, broader multi-site validation is required to confirm its generalizability and to support its integration into feasibility planning.
Accurate workload forecasting enhances trial efficiency, supporting timely, high-quality studies, and accelerating access to new treatments.
临床试验方案的复杂性不断升级，极大地增加了研究协调员的工作量，加剧了人员短缺问题，并导致运营效率低下。这些挑战在资源不足和为少数族裔服务的研究机构中尤为突出，有限的能力可能会阻碍试验的实施。早期准确估计研究协调员的工作量对于有效规划、资源管理以及成功开展临床试验至关重要。
本研究评估采用的安大略方案评估水平（OPAL）评分在预测协调员工作量以改善临床研究运营规划方面的准确性。
在一所历史悠久的黑人学院和大学医学院进行了为期12个月的前瞻性观察研究。七名协调员记录了七项正在积极招募受试者的干预性试验的工作时长。使用已发表的、改编后的OPAL参考表计算估计工作量，并通过描述性统计和配对检验将其与实际工作时长进行比较。为确保基准一致，采用了工作日等效时长（机构标准为7.5小时，行业标准为8小时）。
估计工作时长与实际工作时长之间无统计学显著差异，平均差异为24.1小时（=0.761）。平均绝对误差为167.0小时，约相当于一个月的全职工作时长。
改编后的OPAL评分提供了一个实用工具，可用于估计协调员工作量，并使人员配备与方案复杂性相匹配，包括在资源不足的环境中。然而，需要更广泛的多中心验证来确认其普遍性，并支持将其纳入可行性规划。
准确的工作量预测可提高试验效率，支持及时开展高质量研究，并加速新治疗方法的获取。
