临床试验场所的资源管理和能力规划
Kesley Tyson1, Jillian Harvey2, Leila Forney3
1Clinical Research Center, Morehouse School of Medicine, Atlanta, Georgia, United States of America.
Journal of clinical and translational research
|July 22, 2025
概括
一个适应的协议复杂度得分预测临床试验协调员的努力,改善资源分配和减少倦怠. 这有助于管理工作量并提高临床研究中的工作满意度.
科学领域:
- 临床研究行动临床研究行动.
- 医疗保健管理的管理
- 临床试验协调临床试验协调
背景情况:
- 临床试验协调至关重要,自2020年以来,临床试验激增了30%,增加了需求.
- 由于工作负载和责任的增加,全国仍然缺乏合格的协调员.
- 目前的工具,如安大略协议评估水平 (OPAL),并不能完全捕捉影响协调员生产力的因素.
研究的目的:
- 通过整合协调员努力数据来完善OPAL得分.
- 改善临床试验中的资源分配和运营效率.
- 提高工作满意度,减少临床试验协调员的倦怠和流动.
主要方法:
- 分析了积极招募的干预研究与协调员努力跟踪 (2022年6月至2022年12月).
- 使用适应的协议评估工具来对协议复杂性进行分级.
- 使用描述性统计学,学生的t测试,单变量分析和线性回归来分析数据.
主要成果:
- 分析了七个方案:5 (71%) 联邦资助,2 (29%) 工业赞助,4 (57%) 行为,3 (43%) 药物研究.
- 在行业资助与联邦资助的研究 (P < 0.0001) 和行为与药物研究 (P < 0.0001) 之间,发现了复杂性得分的显著差异.
- 调整后的OPAL得分显著预测了协调员工作时间 (R2 = 0.78,P = 0.01).
结论:
- 适应的协议复杂度得分有效预测协调员的努力.
- 该工具有助于客观评估能力和项目分配.
- 研究结果支持更精确的资源分配,用于更高质量的研究和改善患者安全.
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