School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom.
Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom.
PLoS One. 2024 Aug 20;19(8):e0309308. doi: 10.1371/journal.pone.0309308. eCollection 2024.
Digitisation of patient records, coupled with a moral imperative to use routinely collected data for research, necessitate effective data governance that both facilitates evidence-based research and minimises associated risks. The Generalisable Overview of Study Risk for Lead Investigators Needing Guidance (GOSLING) provides the first quantitative risk-measure for assessing the data-related risks of clinical research projects.
GOSLING employs a self-assessment designed to standardise risk assessment, considering various domains, including data type, security measures, and public co-production. The tool categorises projects into low, medium, and high-risk tiers based on a scoring system developed with the input of patient and public members. It was validated using both real and synthesised project proposals to ensure its effectiveness at triaging the risk of requests for health data.
The tool effectively distinguished between fifteen low, medium, and high-risk projects in testing, aligning with subjective expert assessments. An interactive interface and an open-access policy for the tool encourage researchers to self-evaluate and mitigate risks prior to submission for data governance review. Initial testing demonstrated its potential to streamline the review process by identifying projects that may require less scrutiny or those that pose significant risks.
GOSLING represents the first quantitative approach to measuring study risk, answering calls for standardised risk assessments in using health data for research. Its implementation could contribute to advancing ethical data use, enhancing research transparency, and promoting public trust. Future work will focus on expanding its applicability and exploring its impact on research efficiency and data governance practices.
患者记录的数字化，加上出于道德义务必须使用常规收集的数据进行研究，这就需要有效的数据治理，既能促进循证研究，又能将相关风险降到最低。通用研究风险概述（GOSLING）为需要指导的首席研究员提供了第一个用于评估临床研究项目数据相关风险的定量风险衡量标准。
GOSLING 使用自我评估来标准化风险评估，考虑了各种领域，包括数据类型、安全措施和公众共同参与。该工具根据与患者和公众成员共同开发的评分系统，将项目分为低、中、高风险等级。它使用真实和综合项目提案进行了验证，以确保其在甄别健康数据请求风险方面的有效性。
该工具在测试中有效地区分了十五个低、中、高风险项目，与主观专家评估一致。该工具的交互界面和开放获取政策鼓励研究人员在提交数据治理审查之前进行自我评估和降低风险。初步测试表明，它有可能通过识别可能需要较少审查或存在重大风险的项目来简化审查过程。
GOSLING 代表了衡量研究风险的第一种定量方法，满足了使用健康数据进行研究时进行标准化风险评估的呼吁。它的实施有助于推进道德数据使用、提高研究透明度和促进公众信任。未来的工作将集中于扩大其适用性，并探索其对研究效率和数据治理实践的影响。
