在初级保健中进行多中心随机对照试验:PULsE-AI试验研究人员的学习和建议
Kevin G Pollock1, Carissa Dickerson2, Manjit Kainth3
1Bristol Myers Squibb Pharmaceutical Ltd, Uxbridge, UK.
BMC primary care
|January 3, 2024
概括
使用机器学习算法 (PULsE-AI) 试验对未被诊断的心房的预测进行反思,确定了初级保健研究的关键挑战,包括招聘和技术使用. 分享这些经验教训可以改善未来在初级保健机构的临床试验.
科学领域:
- 初级保健研究初级保健研究
- 临床试验方法论 临床试验方法论
- 医疗信息学 医疗信息学
背景情况:
- 在初级保健中有效的翻译研究面临着挑战.
- 很少有试验正式反映和传播学习.
- COVID-19 流行病影响了多中心随机对照试验.
研究的目的:
- 反思完成多中心随机对照试验的经验.
- 为初级保健研究人员分享挑战,经验教训和建议.
- 提高未来初级保健研究的价值和效率.
主要方法:
- 使用机器学习算法 (PULsE-AI) 试验预测未诊断的心房动的研究人员参与的定性反射练习.
- 与试验指导委员会 (TSC) 合作的半结构化焦点小组.
- 与主要研究人员和实践研究小组进行半结构化采访,然后进行反射性的专题分析.
主要成果:
- 招聘成为一个重大挑战,早于流行病破坏.
- 主要主题包括参与者激励的必要性.
- 报告指出,在采用技术方面存在挑战,特别是在老年人群中.
结论:
- 审判后的正式反思为初级保健研究提供了有价值的见解.
- 分享学习支持临床医生进行有影响力的初级保健试验.
- 优化未来的试验可以提高知识,简化途径,并使患者受益.
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