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DOI: 10.1055/s-0041-1731004
The Cosmos Collaborative: A Vendor-Facilitated Electronic Health Record Data Aggregation Platform
Funding Y.T. and D.K. report support by the Clinical and Translational Science Collaborative (CTSC) of Cleveland which is funded by the National Institutes of Health (NIH), National Center for Advancing Translational Science (NCATS), Clinical and Translational Science Award (CTSA) grant, UL1TR002548. The content is solely the responsibility of the authors and do not necessarily represent the official views of the NIH.
Abstract
Objective Learning healthcare systems use routinely collected data to generate new evidence that informs future practice. While implementing an electronic health record (EHR) system can facilitate this goal for individual institutions, meaningfully aggregating data from multiple institutions can be more empowering. Cosmos is a cross-institution, single EHR vendor-facilitated data aggregation tool. This work aims to describe the initiative and illustrate its potential utility through several use cases.
Methods Cosmos is designed to scale rapidly by leveraging preexisting agreements, clinical health information exchange networks, and data standards. Data are stored centrally as a limited dataset, but the customer facing query tool limits results to prevent patient reidentification.
Results In 2 years, Cosmos grew to contain EHR data of more than 60 million patients. We present practical examples illustrating how Cosmos could further efforts in chronic disease surveillance (asthma and obesity), syndromic surveillance (seasonal influenza and the 2019 novel coronavirus), immunization adherence and adverse event reporting (human papilloma virus and measles, mumps, rubella, and varicella vaccination), and health services research (antibiotic usage for upper respiratory infection).
Discussion A low barrier of entry for Cosmos allows for the rapid accumulation of multi-institutional and mostly de-duplicated EHR data to power research and quality improvement queries characteristic of learning healthcare systems. Limitations are being vendor-specific, an “all or none” contribution model, and the lack of control over queries run on an institution's healthcare data.
Conclusion Cosmos provides a model for within-vendor data standardization and aggregation and a steppingstone for broader intervendor interoperability.
Keywords
electronic health record - data aggregation - research network - health information exchange - collaborationProtection of Human and Animal Subjects
Since data returned from Cosmos is de-identified and presented in aggregate, Cosmos queries do not constitute human subjects research and so do not require institutional review board approval for research purposes.
Authors' Contributions
All listed authors provided substantial contributions to the conception of the work, as well as the analysis and interpretation of data for the work. All listed authors were involved in drafting and approving the final manuscript, and agree to be accountable for all aspects of the work.
Publication History
Received: 14 February 2021
Accepted: 13 April 2021
Article published online:
30 June 2021
© 2021. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)
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