Delivering on NIH data sharing requirements: Avoiding Open Data in Appearance only

• Type of Material: Article
• Authors: Hope Watson, DBT Labs, Boston; Jack Gallifant, Guy's and St Thomas' Hospitals NHS Trust, London, UK; Yuan Lai, Tsinghua University, Beijing, China; Alexander P Radunsky, University of Texas Southwestern Medical Center; Cleva Villanueva, Instituto Politécnico Nacional, Ciudad de Mexico, Mexico; Nicole Martinez, Stanford University; Judy Gichoya, Emory University; Uyen Kim Huynh, UNICEF, New York; Leo Anthony Celi, Harvard-MIT Health Sciences and Technology
• Language: English
• Date: 2023-06-21
• Publisher: BMJ Publishing Group Ltd
• Publication Version: Final Published Version
• Copyright Statement: © 2022 BMJ Publishing Group Ltd & BCS, the Chartered Institute for IT. All rights reserved.
• License: Creative Commons Attribution-NonCommercial 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1136/bmjhci-2023-100771
• Title of Journal or Parent Work: BMJ Health and Care Informatics
• Volume: 30
• Issue: 1
• Grant/Funding Information: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
• Keywords: National Institutes of Health (U.S.); Humans; COVID-19; Data Science; United States; Reproducibility of Results; Information Dissemination; Pandemics; Health information exchange
• Research Categories: Health Sciences, Medicine and Surgery; Psychology, Physiological; Urban and Regional Planning; Health Sciences, Radiology; Health Sciences, Public Health

Abstract

Introduction In January, the National Institutes of Health (NIH) implemented a Data Management and Sharing Policy aiming to leverage data collected during NIH-funded research. The COVID-19 pandemic illustrated that this practice is equally vital for augmenting patient research. In addition, data sharing acts as a necessary safeguard against the introduction of analytical biases. While the pandemic provided an opportunity to curtail critical research issues such as reproducibility and validity through data sharing, this did not materialise in practice and became an example of € Open Data in Appearance Only' (ODIAO). Here, we define ODIAO as the intent of data sharing without the occurrence of actual data sharing (eg, material or digital data transfers). Objective Propose a framework that states the main risks associated with data sharing, systematically present risk mitigation strategies and provide examples through a healthcare lens. Methods This framework was informed by critical aspects of both the Open Data Institute and the NIH's 2023 Data Management and Sharing Policy plan guidelines. Results Through our examination of legal, technical, reputational and commercial categories, we find barriers to data sharing ranging from misinterpretation of General Data Privacy Rule to lack of technical personnel able to execute large data transfers. From this, we deduce that at numerous touchpoints, data sharing is presently too disincentivised to become the norm. Conclusion In order to move towards Open Data, we propose the creation of mechanisms for incentivisation, beginning with recentring data sharing on patient benefits, additional clauses in grant requirements and committees to encourage adherence to data reporting practices.