Governing real-world health data as a public utility.
Published In: Science, 2026, v. 391, n. 6789. P. 993 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Haendel, Melissa A.; Ahern, Ryan; Bailey, Kasie B.; Bakas, Spyridon; Barth-Jones, Daniel C.; Bohl, Alex; Bian, Jiang; Bourne, Philip E.; Boyles, Rebecca R.; Chute, Christopher G.; Cimino, James J.; Grannis, Shaun; Hartman, Terry S.; Holko, Michelle; Hotaling, Nathan A.; Housman, Dan J.; Hunter, Lawrence E.; Hurwitz, Eric; Phua, Jasmin; Kahn, Michael G. 3 of 3
Abstract
It can take many years for evidence generated in research to influence health care guidelines (1). Meanwhile, the vast data collected during everyday life, particularly during engagement with the health care system, remain largely untapped for public health, precision medicine, postmarket safety, and real-time decision-making (2, 3). These "real-world data" (RWD) remain fragmented, proprietary, noninteroperable, and inconsistently governed. Although some approaches to RWD have demonstrated value in limited settings, their impact has remained constrained by uneven incentives, voluntary compliance, and the absence of routine auditability of data access and use. To address this, health data should be governed through federated, standards-based, community-driven models that reflect their public benefit, empower patients and communities, and foster public trust and participation. To help achieve these goals, we propose governing health data as essential infrastructure by using public utility models, defined by their public good, distributed stewardship, and public oversight. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Science. 2026/03, Vol. 391, Issue 6789, p993
- Document Type:Article
- Subject Area:Economics
- Publication Date:2026
- ISSN:0036-8075
- DOI:10.1126/science.aeb1178
- Accession Number:192125659
- Copyright Statement:Copyright of Science is the property of American Association for the Advancement of Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.