The Use of Online Consultation Systems or Remote Consulting in England Characterized Through the Primary Care Health Records of 53 Million People in the OpenSAFELY Platform: Retrospective Cohort Study.


Journal

JMIR public health and surveillance
ISSN: 2369-2960
Titre abrégé: JMIR Public Health Surveill
Pays: Canada
ID NLM: 101669345

Informations de publication

Date de publication:
18 Sep 2024
Historique:
received: 14 02 2023
accepted: 25 07 2023
revised: 18 07 2023
medline: 18 9 2024
pubmed: 18 9 2024
entrez: 18 9 2024
Statut: epublish

Résumé

The National Health Service (NHS) Long Term Plan, published in 2019, committed to ensuring that every patient in England has the right to digital-first primary care by 2023-2024. The COVID-19 pandemic and infection prevention and control measures accelerated work by the NHS to enable and stimulate the use of online consultation (OC) systems across all practices for improved access to primary care. We aimed to explore general practice coding activity associated with the use of OC systems in terms of trends, COVID-19 effect, variation, and quality. With the approval of NHS England, the OpenSAFELY platform was used to query and analyze the in situ electronic health records of suppliers The Phoenix Partnership (TPP) and Egton Medical Information Systems, covering >53 million patients in >6400 practices, mainly in 2019-2020. Systematized Medical Nomenclature for Medicine-Clinical Terminology (SNOMED-CT) codes relevant to OC systems and written OCs were identified including eConsultation. Events were described by volumes and population rates, practice coverage, and trends before and after the COVID-19 pandemic. Variation was characterized among practices, by sociodemographics, and by clinical history of long-term conditions. Overall, 3,550,762 relevant coding events were found in practices using TPP, with the code eConsultation detected in 84.56% (2157/2551) of practices. Activity related to digital forms of interaction increased rapidly from March 2020, the onset of the pandemic; namely, in the second half of 2020, >9 monthly eConsultation coding events per 1000 registered population were registered compared to <1 a year prior. However, we found large variations among regions and practices: December 2020 saw the median practice have 0.9 coded instances per 1000 population compared to at least 36 for the highest decile of practices. On sociodemographics, the TPP cohort with OC instances, when compared (univariate analysis) to the cohort with general practitioner consultations, was more predominantly female (661,235/1,087,919, 60.78% vs 9,172,833/17,166,765, 53.43%), aged 18 to 40 years (349,162/1,080,589, 32.31% vs 4,295,711/17,000,942, 25.27%), White (730,389/1,087,919, 67.14% vs 10,887,858/17,166,765, 63.42%), and less deprived (167,889/1,068,887, 15.71% vs 3,376,403/16,867,074, 20.02%). Looking at the eConsultation code through multivariate analysis, it was more commonly recorded among patients with a history of asthma (adjusted odds ratio [aOR] 1.131, 95% CI 1.124-1.137), depression (aOR 1.144, 95% CI 1.138-1.151), or atrial fibrillation (aOR 1.119, 95% CI 1.099-1.139) when compared to other patients with general practitioner consultations, adjusted for long-term conditions, age, and gender. We successfully queried general practice coding activity relevant to the use of OC systems, showing increased adoption and key areas of variation during the pandemic at both sociodemographic and clinical levels. The work can be expanded to support monitoring of coding quality and underlying activity. This study suggests that large-scale impact evaluation studies can be implemented within the OpenSAFELY platform, namely looking at patient outcomes.

Sections du résumé

BACKGROUND BACKGROUND
The National Health Service (NHS) Long Term Plan, published in 2019, committed to ensuring that every patient in England has the right to digital-first primary care by 2023-2024. The COVID-19 pandemic and infection prevention and control measures accelerated work by the NHS to enable and stimulate the use of online consultation (OC) systems across all practices for improved access to primary care.
OBJECTIVE OBJECTIVE
We aimed to explore general practice coding activity associated with the use of OC systems in terms of trends, COVID-19 effect, variation, and quality.
METHODS METHODS
With the approval of NHS England, the OpenSAFELY platform was used to query and analyze the in situ electronic health records of suppliers The Phoenix Partnership (TPP) and Egton Medical Information Systems, covering >53 million patients in >6400 practices, mainly in 2019-2020. Systematized Medical Nomenclature for Medicine-Clinical Terminology (SNOMED-CT) codes relevant to OC systems and written OCs were identified including eConsultation. Events were described by volumes and population rates, practice coverage, and trends before and after the COVID-19 pandemic. Variation was characterized among practices, by sociodemographics, and by clinical history of long-term conditions.
RESULTS RESULTS
Overall, 3,550,762 relevant coding events were found in practices using TPP, with the code eConsultation detected in 84.56% (2157/2551) of practices. Activity related to digital forms of interaction increased rapidly from March 2020, the onset of the pandemic; namely, in the second half of 2020, >9 monthly eConsultation coding events per 1000 registered population were registered compared to <1 a year prior. However, we found large variations among regions and practices: December 2020 saw the median practice have 0.9 coded instances per 1000 population compared to at least 36 for the highest decile of practices. On sociodemographics, the TPP cohort with OC instances, when compared (univariate analysis) to the cohort with general practitioner consultations, was more predominantly female (661,235/1,087,919, 60.78% vs 9,172,833/17,166,765, 53.43%), aged 18 to 40 years (349,162/1,080,589, 32.31% vs 4,295,711/17,000,942, 25.27%), White (730,389/1,087,919, 67.14% vs 10,887,858/17,166,765, 63.42%), and less deprived (167,889/1,068,887, 15.71% vs 3,376,403/16,867,074, 20.02%). Looking at the eConsultation code through multivariate analysis, it was more commonly recorded among patients with a history of asthma (adjusted odds ratio [aOR] 1.131, 95% CI 1.124-1.137), depression (aOR 1.144, 95% CI 1.138-1.151), or atrial fibrillation (aOR 1.119, 95% CI 1.099-1.139) when compared to other patients with general practitioner consultations, adjusted for long-term conditions, age, and gender.
CONCLUSIONS CONCLUSIONS
We successfully queried general practice coding activity relevant to the use of OC systems, showing increased adoption and key areas of variation during the pandemic at both sociodemographic and clinical levels. The work can be expanded to support monitoring of coding quality and underlying activity. This study suggests that large-scale impact evaluation studies can be implemented within the OpenSAFELY platform, namely looking at patient outcomes.

Identifiants

pubmed: 39292500
pii: v10i1e46485
doi: 10.2196/46485
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e46485

Informations de copyright

©Martina Fonseca, Brian MacKenna, Amir Mehrkar, The OpenSAFELY Collaborative, Caroline E Walters, George Hickman, Jonathan Pearson, Louis Fisher, Peter Inglesby, Seb Bacon, Simon Davy, William Hulme, Ben Goldacre, Ofra Koffman, Minal Bakhai. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 18.09.2024.

Auteurs

Martina Fonseca (M)

NHS England, London, United Kingdom.

Brian MacKenna (B)

NHS England, London, United Kingdom.
Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.

Amir Mehrkar (A)

Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.
See Acknowledgments, Oxford, United Kingdom.

Caroline E Walters (CE)

Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.

George Hickman (G)

Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.

Jonathan Pearson (J)

NHS England, London, United Kingdom.

Louis Fisher (L)

Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.

Peter Inglesby (P)

Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.

Seb Bacon (S)

Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.

Simon Davy (S)

Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.

William Hulme (W)

Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.

Ben Goldacre (B)

Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.

Ofra Koffman (O)

NHS England, London, United Kingdom.

Minal Bakhai (M)

NHS England, London, United Kingdom.

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