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.

译文

背景:临床研究站点以各种复杂性,时间表和不断变化的工作量进行试验。尽管首席研究人员(PI)最终负责试验的内容和进行,但他们仍然严重依赖现场人员按照良好临床实践(GCP)准则成功注册并完成研究。现场工作人员的主要力量是分配了试验的临床研究协调员(CRC)。这些CRC同时进行许多研究。管理研究任务和工作量是一项艰巨的任务,需要了解试验的复杂性,预期的入学率以及许多其他影响绩效的因素。
方法:传统的分配工作量给现场人员的方法是通过考虑试验参与者的数量来量化试验的复杂性并估算工作时间。但是,这并未考虑相关工作量或人员属性变化的影响。它还忽略了影响性能的其他因素,并假定所有参与者的注册人数和试验完成人数都达到了上限。本文介绍了一种新颖的方法,该方法无需费力即可确定协议复杂度对CRC生产率的影响。这些指标可以评估CRC的表现如何受到分配的研究数量的影响。
结果:通过了解工作负载分配对CRC生产率和容量的影响,站点管理员可以使用算法方法来提高性能。该过程考虑了站点管理员控制范围之内和之外的因素。
结论:站点可从分析中受益,该分析可测量CRC如何适应研究复杂性随时间推移对累积工作量的影响。优化生产力还意味着要符合GCP准则并避免员工精疲力尽。随着研究变得越来越困难,站点管理员需要工具来管理复杂性并平衡员工之间的工作量。

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