| Title: |
The Cosmos Collaborative: A Vendor-Facilitated Electronic Health Record Data Aggregation Platform |
| Authors: |
Tarabichi, Yasir; Frees, Adam; Honeywell, Steven; Huang, Courtney; Naidech, Andrew M.; Moore, Jason H.; Kaelber, David C. |
| Contributors: |
Clinical and Translational Science Collaborative (CTSC) of Cleveland; National Institutes of Health (NIH), National Center for Advancing Translational Science (NCATS), Clinical and Translational Science Award |
| Source: |
ACI Open ; volume 05, issue 01, page e36-e46 ; ISSN 2566-9346 |
| Publisher Information: |
Georg Thieme Verlag KG |
| Publication Year: |
2021 |
| Description: |
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. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.1055/s-0041-1731004 |
| DOI: |
10.1055/s-0041-1731004.pdf |
| Availability: |
https://doi.org/10.1055/s-0041-1731004; http://www.thieme-connect.de/products/ejournals/pdf/10.1055/s-0041-1731004.pdf |
| Rights: |
https://creativecommons.org/licenses/by/4.0/ |
| Accession Number: |
edsbas.D257937C |
| Database: |
BASE |