需要指导的首席调查人员研究风险的概括概述 (GOSLING):数据治理风险工具
Anmol Arora1, Adam Loveday2, Sarah Burge3
1School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom.
PloS one
|August 20, 2024
概括
需要指导的首席研究人员研究风险的概括概述 (GOSLING) 工具提供了评估健康数据研究风险的第一个定量措施. 这种标准化的方法有助于数据的伦理使用,并简化治理审查.
科学领域:
- 医疗信息学 医疗信息学
- 临床研究治理治理 临床研究治理
- 数据管理数据管理
背景情况:
- 数字化患者记录和对常规收集的数据进行研究的伦理使用需要强大的数据治理框架.
- 在临床研究中评估数据相关风险的现有方法往往是定性性的,缺乏标准化.
研究的目的:
- 引入需要指导的首席研究人员研究风险的可概括概述 (GOSLING),这是临床研究中数据相关风险的第一个定量风险测量工具.
- 为研究人员提供标准化的自我评估工具,以评估和减轻与健康数据研究项目相关的风险.
主要方法:
- GOSLING使用一个自我评估问卷,涵盖数据类型,安全性和公众参与,将项目分为低风险,中风险或高风险级别.
- 一个得分系统,与患者和公众投入开发,支风险分类.
- 该工具使用真实和合成的项目提案进行了验证,以确认其在对健康数据访问请求进行分类方面的有效性.
主要成果:
- 在验证过程中,GOSLING工具成功地区分了15个低风险,中风险和高风险项目,与专家评估保持一致.
- 在正式的数据治理审查之前,一个交互式的开放访问界面鼓励研究人员主动评估和减轻风险.
- 最初的测试表明,GOSLING可以通过识别需要更少审查或具有重大风险的项目来加速审查过程.
结论:
- 戈斯林创建了一种新的定量方法来研究风险评估,解决了对健康数据研究标准化方法的需求.
- 实施GOSLING可以促进数据的伦理利用,提高研究透明度,并促进公众的信任.
- 未来的研究将集中在扩大GOSLING的应用和评估其对研究效率和数据治理的影响上.
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