Paprica P Alison, Sutherland Eric, Smith Andrea, Brudno Michael, Cartagena Rosario G, Crichlow Monique, Courtney Brian K, Loken Chris, McGrail Kimberlyn M, Ryan Alex, Schull Michael J, Thorogood Adrian, Virtanen Carl, Yang Kathleen
University of Toronto, Institute of Health Policy, Management and Evaluation, 155 College Street, Toronto, ON, M5T 3M6, Canada.
Vector Institute, Suite 710, 661 University Ave, Toronto, ON, M5G 1M1, Canada.
Int J Popul Data Sci. 2020 Aug 24;5(1):1353. doi: 10.23889/ijpds.v5i1.1353.
Increasingly, the label "data trust" is being applied to repeatable mechanisms or approaches to sharing data in a timely, fair, safe, and equitable way. However, there is an absence of practical guidance regarding how to establish and operate a data trust.
In December 2019, the Canadian Institute for Health Information and the Vector Institute for Artificial Intelligence convened a working meeting of 19 people representing 15 Canadian organizations/initiatives involved in data sharing, most of which focus on public sector health data. The objective was to identify essential requirements for the establishment and operation of data trusts in the Canadian context. Preliminary requirements were discussed during the meeting and then refined as authors contributed to this manuscript.
Twelve minimum specification requirements ("min specs") for data trusts were identified. The foundational min spec is that data trusts must meet all legal requirements, including legal authority to collect, hold or share data. In addition, there was agreement that data trusts must have (i) an accountable governing body to ensure that the data trust achieves its stated purpose and is transparent, (ii) comprehensive data management including clear processes and qualified individuals responsible for the collection, storage, access, disclosure and use of data, (iii) training and accountability requirements for all data users and (iv) ongoing public and stakeholder engagement.
Practical guidance for the establishment and operation of data trusts was articulated in the form of 12 min specs requirements. The 12 min specs are a starting point. Future work to refine and strengthen them with members of the public, companies, and additional research data stakeholders from within and outside of Canada, is recommended.
“数据信托”这一标签越来越多地被用于指代以及时、公平、安全和公平的方式共享数据的可重复机制或方法。然而，关于如何建立和运营数据信托，目前缺乏实际指导。
2019年12月，加拿大卫生信息研究所和向量人工智能研究所召集了一次工作会议，与会的19人代表了15个参与数据共享的加拿大组织/倡议，其中大多数专注于公共部门的健康数据。目的是确定在加拿大背景下建立和运营数据信托的基本要求。会议期间讨论了初步要求，随后随着作者对本手稿的贡献而进行了完善。
确定了数据信托的12项最低规范要求（“最低规范”）。基本的最低规范是数据信托必须符合所有法律要求，包括收集、持有或共享数据的法律权限。此外，与会者一致认为，数据信托必须具备：（i）一个可问责的治理机构，以确保数据信托实现其既定目标并保持透明；（ii）全面的数据管理，包括明确的流程以及负责数据收集、存储、访问、披露和使用的数据管理人员；（iii）对所有数据用户的培训和问责要求；（iv）持续的公众和利益相关者参与。
以12项最低规范要求的形式阐述了数据信托建立和运营的实际指导。这12项最低规范是一个起点。建议未来与加拿大国内外的公众、公司以及其他研究数据利益相关者共同完善和强化这些规范。
