Tassé Anne-Marie, Kirby Emily, Fortier Isabel
1 Public Population Project in Genomics and Society, McGill University and Genome Quebec Innovation Centre , Montreal, Quebec, Canada .
2 Maelstrom Research, Research Institute of the McGill University Health Centre , Montreal, Quebec, Canada .
Biopreserv Biobank. 2016 Jun;14(3):249-55. doi: 10.1089/bio.2015.0122. Epub 2016 Apr 26.
The past decade has witnessed the creation of major international research consortia, aiming to facilitate the sharing of data from different studies to maximize health benefits. However, combining heterogeneous data across existing studies requires addressing issues related to both data harmonization and ethical and legal interoperability. This article proposes a rigorous interoperability assessment process to assess whether different data sets are sufficiently ethically and legally interoperable to allow for a given proposed research use. The methodology used to develop this process is based on a comprehensive analysis of the international ethical and legal framework governing the use of retrospective data in research, and includes the following steps: (I) finding existing processes; (II) comparing processes to identify similarities and differences and determining the limits of the "consistent whole"; (III) establishing common principles and procedures; and, (IV) changing or removing processes that do not contribute to the consistent whole. Each of these four steps were examined using step-specific methodologies, including (a) literature and policy reviews; (b) consultations with international ethical, legal and social implications (ELSI) experts; and (c) a case study piloting the proposed framework in an actual international research consortium. This assessment process takes into account key legal and ethical components such as consent, recontact, and waiver of consent. As a result, this analysis allows the development of a comprehensive filter used to verify the legal and ethical restrictions pertaining to a data set. This in turns helps in determining whether the given data set can to be used for a proposed research project, or is ethically and legally interoperable for use in research collaborations. By integrating this filter to the regular data access processes used by cohorts, not only will researchers be able to create virtual "mega-cohorts" of research participants, but it will also ensure that these cohorts respect basic legal and ethical precepts.
过去十年见证了主要国际研究联盟的创立，其旨在促进不同研究数据的共享以实现最大健康效益。然而，整合现有研究中的异质数据需要解决与数据协调以及伦理和法律互操作性相关的问题。本文提出了一个严格的互操作性评估流程，以评估不同数据集在伦理和法律上是否足够可互操作，从而允许进行特定的拟议研究用途。用于开发此流程的方法基于对研究中回顾性数据使用的国际伦理和法律框架的全面分析，包括以下步骤：（I）查找现有流程；（II）比较流程以识别异同并确定“一致整体”的界限；（III）确立共同原则和程序；以及（IV）更改或去除对一致整体无贡献的流程。这四个步骤中的每一步都使用特定步骤的方法进行了检验，包括（a）文献和政策审查；（b）与国际伦理、法律和社会影响（ELSI）专家进行磋商；以及（c）在一个实际的国际研究联盟中对拟议框架进行试点的案例研究。此评估流程考虑了诸如同意、再次联系和同意豁免等关键法律和伦理要素。因此，该分析允许开发一个全面的筛选工具，用于核实与数据集相关的法律和伦理限制。这进而有助于确定给定数据集是否可用于拟议的研究项目，或者在伦理和法律上是否可互操作以用于研究合作。通过将此筛选工具整合到队列使用的常规数据访问流程中，研究人员不仅能够创建研究参与者的虚拟“超级队列”，而且还将确保这些队列遵守基本的法律和伦理准则。
