Cumulus: a federated electronic health record-based learning system powered by Fast Healthcare Interoperability Resources and artificial intelligence.

Autor:	McMurry AJ; Computational Health Informatics Program, Boston Children's Hospital, Boston, MA 02215, United States.; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, United States., Gottlieb DI; Computational Health Informatics Program, Boston Children's Hospital, Boston, MA 02215, United States.; Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, United States., Miller TA; Computational Health Informatics Program, Boston Children's Hospital, Boston, MA 02215, United States.; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, United States., Jones JR; Computational Health Informatics Program, Boston Children's Hospital, Boston, MA 02215, United States., Atreja A; Innovation Technology, UC Davis Health, Rancho Cordova, CA 95670, United States., Crago J; Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, IN 46202, United States., Desai PM; Department of Internal Medicine, Rush University Medical Center, Chicago, IL 60612, United States., Dixon BE; Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, IN 46202, United States.; Department of Health Policy and Management, Fairbanks School of Public Health, Indiana University, Indianapolis, IN 46202, United States., Garber M; Computational Health Informatics Program, Boston Children's Hospital, Boston, MA 02215, United States., Ignatov V; Computational Health Informatics Program, Boston Children's Hospital, Boston, MA 02215, United States., Kirchner LA; CDC Foundation, Atlanta, GA 30308, United States., Payne PRO; Institute for Informatics, Data Science, and Biostatistics, Washington University School of Medicine in St Louis, St Louis, MO 63110, United States.; Department of Medicine, Washington University School of Medicine in St Louis, St Louis, MO 63110, United States., Saldanha AJ; Department of Health Innovation, Rush University Medical Center, Chicago, IL 60612, United States., Shankar PRV; Innovation Technology, UC Davis Health, Rancho Cordova, CA 95670, United States.; Department of Public Health Sciences, UC Davis Health, Davis, CA 95817, United States., Solad YV; Innovation Technology, UC Davis Health, Rancho Cordova, CA 95670, United States., Sprouse EA; Double Lantern Informatics, Atlanta, GA 30305, United States., Terry M; Computational Health Informatics Program, Boston Children's Hospital, Boston, MA 02215, United States., Wilcox AB; Institute for Informatics, Data Science, and Biostatistics, Washington University School of Medicine in St Louis, St Louis, MO 63110, United States.; Department of Medicine, Washington University School of Medicine in St Louis, St Louis, MO 63110, United States., Mandl KD; Computational Health Informatics Program, Boston Children's Hospital, Boston, MA 02215, United States.; Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, United States.
Jazyk:	angličtina
Zdroj:	Journal of the American Medical Informatics Association : JAMIA [J Am Med Inform Assoc] 2024 Aug 01; Vol. 31 (8), pp. 1638-1647.
DOI:	10.1093/jamia/ocae130
Abstrakt:	Objective: To address challenges in large-scale electronic health record (EHR) data exchange, we sought to develop, deploy, and test an open source, cloud-hosted app "listener" that accesses standardized data across the SMART/HL7 Bulk FHIR Access application programming interface (API). Methods: We advance a model for scalable, federated, data sharing and learning. Cumulus software is designed to address key technology and policy desiderata including local utility, control, and administrative simplicity as well as privacy preservation during robust data sharing, and artificial intelligence (AI) for processing unstructured text. Results: Cumulus relies on containerized, cloud-hosted software, installed within a healthcare organization's security envelope. Cumulus accesses EHR data via the Bulk FHIR interface and streamlines automated processing and sharing. The modular design enables use of the latest AI and natural language processing tools and supports provider autonomy and administrative simplicity. In an initial test, Cumulus was deployed across 5 healthcare systems each partnered with public health. Cumulus output is patient counts which were aggregated into a table stratifying variables of interest to enable population health studies. All code is available open source. A policy stipulating that only aggregate data leave the institution greatly facilitated data sharing agreements. Discussion and Conclusion: Cumulus addresses barriers to data sharing based on (1) federally required support for standard APIs, (2) increasing use of cloud computing, and (3) advances in AI. There is potential for scalability to support learning across myriad network configurations and use cases. (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)
Databáze:	MEDLINE
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