The Challenges and Lessons Learned Building a New UK Infrastructure for Finding and Accessing Population-Wide COVID-19 Data for Research and Public Health Analysis: The CO-CONNECT Project
The COVID-19-Curated and Open Analysis and Research Platform (CO-CONNECT) project worked with 22 organizations across the United Kingdom to build a federated platform, enabling researchers to instantaneously and dynamically query federated datasets to find relevant data for their study. Finding relevant data takes time and effort, reducing the efficiency of research. Although data controllers could understand the value of such a system, there were significant challenges and delays in setting up the platform in response to COVID-19. This paper aims to present the challenges and lessons learned from the CO-CONNECT project to support other similar initiatives in the future. The project encountered many challenges, including the impacts of lockdowns on collaboration, understanding the new architecture, competing demands on people's time during a pandemic, data governance approvals, different levels of technical capabilities, data transformation to a common data model, access to granular-level laboratory data, and how to engage public and patient representatives meaningfully on a highly technical project. To overcome these challenges, we developed a range of methods to support data partners such as explainer videos; regular, short, "touch base" videoconference calls; drop-in workshops; live demos; and a standardized technical onboarding documentation pack. A 4-stage data governance process emerged. The patient and public representatives were fully integrated team members. Persistence, patience, and understanding were key. We make 8 recommendations to change the landscape for future similar initiatives. The new architecture and processes developed are being built upon for non-COVID-19-related data, providing an infrastructural legacy.
Authors
- Gordon Milligan (ORCID: https://orcid.org/0000-0002-1171-5234)
- Jillian Beggs (ORCID: https://orcid.org/0000-0002-7591-3256)
- Tom Giles (ORCID: https://orcid.org/0000-0003-1356-4289)
- Antony Chuter (ORCID: https://orcid.org/0000-0002-0646-5939)
- Aziz Sheikh (ORCID: https://orcid.org/0000-0001-7022-3056)
- Shahzad Mumtaz (ORCID: https://orcid.org/0000-0003-2606-2405)
- Susan Hopkins (ORCID: https://orcid.org/0000-0001-5179-5702)
- Esmond Urwin (ORCID: https://orcid.org/0000-0003-4626-2172)
- Scott Horban (ORCID: https://orcid.org/0000-0001-6431-8243)
- Erum Masood (ORCID: https://orcid.org/0000-0001-8790-5352)
- Gerry Reilly (ORCID: https://orcid.org/0000-0003-1427-3598)
- Joseph Best (ORCID: https://orcid.org/0000-0001-6809-4179)
- Andrew D. Morris (ORCID: https://orcid.org/0000-0002-1766-0473)
- Emily Jefferson (ORCID: https://orcid.org/0000-0003-2992-7582)
- David Seymour (ORCID: https://orcid.org/0000-0003-4194-2056)
- Sam Cox (ORCID: https://orcid.org/0000-0002-9841-9816)
- Philip Quinlan (ORCID: https://orcid.org/0000-0002-3012-6646)
- Jenny Johnstone (ORCID: https://orcid.org/0000-0002-6755-1267)
- Christian Cole (ORCID: https://orcid.org/0000-0002-2560-2484)
Institutions
- University of Nottingham (GB)
- University of Dundee (GB)
- Public Health England (GB)
- United States House of Representatives (US)
- Health Data Research UK (GB)
- Genomics (United Kingdom) (GB)
- University of Edinburgh (GB)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2024-11-20
- DOI
- https://doi.org/10.2196/50235
- Citations
- 5
- Primary Topic
- Data-Driven Disease Surveillance
- Type
- article
- Field-Weighted Citation Impact
- 1.93
Funders
- Scottish Government
- UK Research and Innovation
- National Institute for Health and Care Research
- British Heart Foundation
- Department of Health and Social Care
- Public Health Agency
- Chief Scientist Office, Scottish Government Health and Social Care Directorate
- Medical Research Council
- Engineering and Physical Sciences Research Council
- Economic and Social Research Council
- Health and Social Care Research and Development Division