July 15, 2019
2 min read

EULAR stresses privacy, transparency in research using 'big data'

You've successfully added to your alerts. You will receive an email when new content is published.

Click Here to Manage Email Alerts

We were unable to process your request. Please try again later. If you continue to have this issue please contact customerservice@slackinc.com.

Laure Gossec

Rheumatic and musculoskeletal disorders researchers should consider the ethical issues surrounding privacy, confidentiality, identity and transparency when using “big data,” according to a paper published in the Annals of the Rheumatic Diseases.

“Big data is a concept that has captured the attention of the medical community, but recommendations from medical societies and other authorities on how to leverage big data are not yet commonplace,” Laure Gossec, MD, PhD, of Sorbonne University in Paris, told Healio Rheumatology. “The term ‘big data’ itself has a broad definition, and could define the large-scale data sets that include imaging data, electronic health records or administrative claim records, among others. Big data is sometimes also used to refer to specific analytics and statistical methods, such as artificial intelligence and machine learning.”

To guide collection and use of big data in rheumatic and musculoskeletal disease research, Gossec and a task force of 14 experts drafted a series of “points to consider.” The task force — representing eight European countries — included six rheumatologists, four data scientists, one cardiologist specialized in systems medicine, one patient research partner, one health professional with experience in outcomes research and one rheumatology fellow. In developing these points, the group met in October 2018 to discuss the primary questions they aimed to address, and then, between November 2018 and February 2019, conducted a literature review.

Researchers should consider the ethical issues surrounding privacy, confidentiality, identity and transparency when using “big data,” according to a paper.
Source: Adobe

The task force met face-to-face in February 2019 to discuss the results of the literature review and draft overarching principles and the specific points to consider. At this meeting, and in subsequent online discussions, the task force members voted on their level of agreement with each point. The final draft was reviewed and approved by all members of the task force and the EULAR executive committee.

The group ultimately developed and approved three overarching principles and 10 points to consider. The overarching principles are:

  • Ethical issues regarding privacy, confidentiality, identity and transparency are “key principles to consider” for all use of big data;
  • Big data represents an unprecedented opportunity to realize “transformative discoveries” in rheumatology research and practice; and
  • The goal of using big data in rheumatology and musculoskeletal disease is improving the health, lives and care of patients.

In their points to consider, the task force wrote that the use of global harmonized and comprehensive standards should be promoted to promote the interoperability of big data. In addition, they stressed that open data platforms are preferable for big data related to rheumatology. They also wrote that privacy must be applied to the collection, processing, storage and study of big data.

“The use of big data by [artificial intelligence], computational modelling and machine learning is a rapidly evolving field with the potential to profoundly modify rheumatic and musculoskeletal disorders research and patient care,” Gossec said. “These first EULAR-endorsed points to consider address key issues including ethics, data sources, data storage, data analyzes, AI, the need for benchmarking, adequate reporting of methods and implementation of findings into clinical practice. We hope these points to consider will promote advances and homogeneity in the field of big data in rheumatic and musculoskeletal disorders and may be useful as guidance in other medical fields.” – by Jason Laday

Disclosure: Gossec reports publishing a study for which Orange IMT performed machine-learning analyses without charge to the author.