Currently accepted at: JMIR Public Health and Surveillance
Date Submitted: Feb 14, 2023
Date Accepted: Jul 25, 2023
This paper has been accepted and is currently in production.
It will appear shortly on 10.2196/46485
The final accepted version (not copyedited yet) is in this tab.
Use of online consultation systems or remote consulting in England characterised though 53 million peoples’ primary care health records in OpenSAFELY: a retrospective cohort study
ABSTRACT
Background:
The pandemic accelerated work by the NHS in England to enable and stimulate use of online consultation systems across all practices, for improved access to primary care.
Objective:
We aimed to explore general practice coding activity associated with the use of online consultation systems in terms of trends, COVID-19 effect, variation and quality.
Methods:
With the approval of NHS England, OpenSAFELY-TPP and OpenSAFELY-EMIS were used to query and analyse in situ records of electronic health record systems of over 53 million patients in over 6,400 practices, mainly in 2019-2020. SNOMED CT codes relevant to online consultation systems and written online consultations were identified. Coded events were described by volumes, practice coverage, trends pre- and post-COVID-19 and inter-practice and sociodemographic variation.
Results:
3,550,762 relevant coding events were found in TPP practices, with code eConsultation detected in 84% of practices. Coding activity related to digital forms of interaction increased rapidly from March 2020 at the onset of the COVID-19 pandemic, though we found large variation in coding instance rates among practices in England. Code instances were more commonly found among females, those aged 18-40, those least deprived or white. eConsultation coded activity was more commonly found recorded among patients with a history of asthma or depression.
Conclusions:
We successfully queried general practice coding activity relevant to the use of online consultation systems, showing increased adoption as well as key areas of variation during the COVID-19 pandemic. The work can be expanded to support monitoring of coding quality and underlying activity. In future, large-scale impact evaluation studies can be implemented within the platform, namely looking at resource utilisation and patient outcomes.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.