FAIR data sharing: The roles of common data elements and harmonization

Kush, Rebecca; Warzel, Denise; Kush, Maura; Sherman, Alexander; Navarro, Eileen A.; Fitzmartin, Ronald D.; Pétavy, Frank; Gálvez, José Luis Vingut; Becnel, Lauren B.; Zhou, Fang Liz; Harmon, Nicole; Jaúregui, Bárbara; Jackson, Tammy; Hudson, Lynn D.

doi:10.1016/j.jbi.2020.103421

Cited by 102 publications

(76 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Regulated clinical research can be used as an illustrative example of the value of global data standards, particularly when time is of the essence during a pandemic or public health crisis. When data are collected differently at the start of a research study, the necessary mapping and interpretation to compare or aggregate data after the fact are costly and time‐consuming; quality may be compromised and precious but uninterpretable data may be lost 12,13 . In contrast, incorporating global industry‐wide data standards into a research project at its outset can reduce the start‐up time for a research study by 70% to 90%, and can correspondingly reduce the time required to create tables and analysis datasets from results across studies by 40% to 60% 14 .…”

Section: Standardized Global Data: Lessons From Clinical Research Formentioning

confidence: 99%

Addressing the Covid‐19 pandemic and future public health challenges through global collaboration and a data‐driven systems approach

Ros

Kush²,

Friedman

et al. 2020

Learning Health Systems

View full text Add to dashboard Cite

Covid‐19 has already taught us that the greatest public health challenges of our generation will show no respect for national boundaries, will impact lives and health of people of all nations, and will affect economies and quality of life in unprecedented ways. The types of rapid learning envisioned to address Covid‐19 and future public health crises require a systems approach that enables sharing of data and lessons learned at scale. Agreement on a systems approach augmented by technology and standards will be foundational to making such learning meaningful and to ensuring its scientific integrity. With this purpose in mind, a group of individuals from Spain, Italy, and the United States have formed a transatlantic collaboration, with the aim of generating a proposed comprehensive standards‐based systems approach and data‐driven framework for collection, management, and analysis of high‐quality data. This framework will inform decisions in managing clinical responses and social measures to overcome the Covid‐19 global pandemic and to prepare for future public health crises. We first argue that standardized data of the type now common in global regulated clinical research is the essential fuel that will power a global system for addressing (and preventing) current and future pandemics. We then present a blueprint for a system that will put these data to use in driving a range of key decisions. In the context of this system, we describe and categorize the specific types of data the system will require for different purposes and document the standards currently in use for each of these categories in the three nations participating in this work. In so doing, we anticipate some of the challenges to harmonizing these data but also suggest opportunities for further global standardization and harmonization. While we have scaled this transnational effort to three nations, we hope to stimulate an international dialogue with a culmination of realizing such a system.

show abstract

Section: Standardized Global Data: Lessons From Clinical Research Formentioning

confidence: 99%

Addressing the Covid‐19 pandemic and future public health challenges through global collaboration and a data‐driven systems approach

Ros

Kush²,

Friedman

et al. 2020

Learning Health Systems

View full text Add to dashboard Cite

show abstract

“…Successful patient-level data sharing will continue to require all stakeholders to work together to expand the precompetitive space. Those collecting data should agree to the use of standards and common data elements in the collection of data, and data owners must commit to sharing data and engaging in collaborative partnerships [ 40 ]. Organizations that convene PPPs and consortia must continue to provide safe, cooperative spaces for precompetitive data sharing and respond to concerns of potential data contributors.…”

Section: Discussionmentioning

confidence: 99%

Effective Data Sharing as a Conduit for Advancing Medical Product Development

Karpen

White

Mullin

et al. 2021

Ther Innov Regul Sci

View full text Add to dashboard Cite

Introduction Patient-level data sharing has the potential to significantly impact the lives of patients by optimizing and improving the medical product development process. In the product development setting, successful data sharing is defined as data sharing that is actionable and facilitates decision making during the development and review of medical products. This often occurs through the creation of new product development tools or methodologies, such as novel clinical trial design and enrichment strategies, predictive pre-clinical and clinical models, clinical trial simulation tools, biomarkers, and clinical outcomes assessments, and more. Methods To be successful, extensive partnerships must be established between all relevant stakeholders, including industry, academia, research institutes and societies, patient-advocacy groups, and governmental agencies, and a neutral third-party convening organization that can provide a pre-competitive space for data sharing to occur. Conclusions Data sharing focused on identified regulatory deliverables that improve the medical product development process encounters significant challenges that are not seen with data sharing aimed at advancing clinical decision making and requires the commitment of all stakeholders. Regulatory data sharing challenges and solutions, as well as multiple examples of previous successful data sharing initiatives are presented and discussed in the context of medical product development.

show abstract

“…Data sharing encompasses several diverse concepts, from transparency and regulation, to interoperability (discussed above). A key aspirational goal for data sharing has been, and remains the attainment of interoperability, under the umbrella of the now-recognized FAIR principle (Findable, Accessible, Interoperable and Reusable) 87 . The past decade has seen the emergence of several policies relating to data sharing and research transparency by the Office of Research Integrity, National Institute of Health or other organizations 88 .…”

Section: The Data Ecosystemmentioning

confidence: 99%

Digital pathology and computational image analysis in nephropathology

et al. 2020

View full text Add to dashboard Cite

The emergence of digital pathology-an image-based environment for the acquisition, management and interpretation of pathology information supported by computational techniques for data extraction and analysis-is changing the pathology ecosystem. In particular, by virtue of our new-found ability to generate and curate digital libraries, the field of machine vision can now be effectively applied to histopathological subject matter by individuals who do not have deep expertise in machine vision techniques. Although these novel approaches have already advanced the detection, classification, and prognostication of diseases in the fields of radiology and oncology, renal pathology is just entering the digital era, with the establishment of consortia and digital pathology repositories for the collection, analysis and integration of pathology data with other domains. The development of machine-learning approaches for the extraction of information from image data, allows for tissue interrogation in a way that was not previously possible. The application of these novel tools are placing pathology centre stage in the process of defining new, integrated, biologically and clinically homogeneous disease categories, to identify patients at risk of progression, and shifting current paradigms for the treatment and prevention of kidney diseases.

show abstract

FAIR data sharing: The roles of common data elements and harmonization

Cited by 102 publications

References 7 publications

Addressing the Covid‐19 pandemic and future public health challenges through global collaboration and a data‐driven systems approach

Addressing the Covid‐19 pandemic and future public health challenges through global collaboration and a data‐driven systems approach

Effective Data Sharing as a Conduit for Advancing Medical Product Development

Digital pathology and computational image analysis in nephropathology

Contact Info

Product

Resources

About